General Setup


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[1] "/Users/swvanderlaan/git/CirculatoryHealth/AE_20211201_YAW_SWVANDERLAAN_HDAC9"
 [1] "_archived"                                     "1. AE_20211201_YAW_SWVANDERLAAN_HDAC9.nb.html"
 [3] "1. AE_20211201_YAW_SWVANDERLAAN_HDAC9.Rmd"     "2. SNP_analyses.Rmd"                          
 [5] "20220317.HDAC9.baseline.RData"                 "3. bulkRNAseq.nb.html"                        
 [7] "3. bulkRNAseq.Rmd"                             "4. report.scrnaseq.nb.html"                   
 [9] "4. report.scrnaseq.Rmd"                        "AE_20211201_YAW_SWVANDERLAAN_HDAC9.Rproj"     
[11] "AnalysisPlan"                                  "HDAC9"                                        
[13] "images"                                        "LICENSE"                                      
[15] "README.html"                                   "README.md"                                    
[17] "references.bib"                                "renv"                                         
[19] "renv.lock"                                     "scripts"                                      
[21] "SNP"                                           "targets"                                      
source(paste0(PROJECT_loc, "/scripts/functions.R"))
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Today = format(as.Date(as.POSIXlt(Sys.time())), "%Y%m%d")
Today.Report = format(as.Date(as.POSIXlt(Sys.time())), "%A, %B %d, %Y")

### UtrechtScienceParkColoursScheme
###
### WebsitetoconvertHEXtoRGB:http://hex.colorrrs.com.
### Forsomefunctionsyoushoulddividethesenumbersby255.
###
### No. Color                 HEX   (RGB)                                     CHR         MAF/INFO
###---------------------------------------------------------------------------------------
### 1     yellow                #FBB820 (251,184,32)                      =>    1       or 1.0>INFO
### 2     gold                #F59D10 (245,157,16)                    =>    2       
### 3     salmon                #E55738 (229,87,56)                   =>    3       or 0.05<MAF<0.2 or 0.4<INFO<0.6
### 4     darkpink          #DB003F ((219,0,63)                   =>    4       
### 5     lightpink         #E35493 (227,84,147)                      =>    5       or 0.8<INFO<1.0
### 6     pink                #D5267B (213,38,123)                    =>    6       
### 7     hardpink          #CC0071 (204,0,113)                   =>    7       
### 8     lightpurple       #A8448A (168,68,138)                      =>    8       
### 9     purple                #9A3480 (154,52,128)                      =>    9       
### 10  lavendel            #8D5B9A (141,91,154)                      =>    10      
### 11  bluepurple        #705296 (112,82,150)                    =>    11      
### 12  purpleblue        #686AA9 (104,106,169)               =>    12      
### 13  lightpurpleblue #6173AD (97,115,173/101,120,180)    =>  13      
### 14  seablue             #4C81BF (76,129,191)                      =>    14      
### 15  skyblue             #2F8BC9 (47,139,201)                      =>    15      
### 16  azurblue            #1290D9 (18,144,217)                      =>    16      or 0.01<MAF<0.05 or 0.2<INFO<0.4
### 17  lightazurblue     #1396D8 (19,150,216)                    =>    17      
### 18  greenblue           #15A6C1 (21,166,193)                      =>    18      
### 19  seaweedgreen      #5EB17F (94,177,127)                    =>    19      
### 20  yellowgreen       #86B833 (134,184,51)                    =>    20      
### 21  lightmossgreen  #C5D220 (197,210,32)                      =>    21      
### 22  mossgreen           #9FC228 (159,194,40)                      =>    22      or MAF>0.20 or 0.6<INFO<0.8
### 23  lightgreen      #78B113 (120,177,19)                      =>    23/X
### 24  green                 #49A01D (73,160,29)                     =>    24/Y
### 25  grey                  #595A5C (89,90,92)                        =>  25/XY   or MAF<0.01 or 0.0<INFO<0.2
### 26  lightgrey           #A2A3A4 (162,163,164)                 =>    26/MT
###
### ADDITIONAL COLORS
### 27  midgrey         #D7D8D7
### 28  verylightgrey   #ECECEC"
### 29  white           #FFFFFF
### 30  black           #000000
###----------------------------------------------------------------------------------------------

uithof_color = c("#FBB820","#F59D10","#E55738","#DB003F","#E35493","#D5267B",
                 "#CC0071","#A8448A","#9A3480","#8D5B9A","#705296","#686AA9",
                 "#6173AD","#4C81BF","#2F8BC9","#1290D9","#1396D8","#15A6C1",
                 "#5EB17F","#86B833","#C5D220","#9FC228","#78B113","#49A01D",
                 "#595A5C","#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")

uithof_color_legend = c("#FBB820", "#F59D10", "#E55738", "#DB003F", "#E35493",
                        "#D5267B", "#CC0071", "#A8448A", "#9A3480", "#8D5B9A",
                        "#705296", "#686AA9", "#6173AD", "#4C81BF", "#2F8BC9",
                        "#1290D9", "#1396D8", "#15A6C1", "#5EB17F", "#86B833",
                        "#C5D220", "#9FC228", "#78B113", "#49A01D", "#595A5C",
                        "#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")
### ----------------------------------------------------------------------------

ERA-CVD ‘druggable-MI-targets’

For the ERA-CVD ‘druggable-MI-targets’ project (grantnumber: 01KL1802) we performed two related RNA sequencing (RNAseq) experiments:

  1. conventional (‘bulk’) RNAseq using RNA extracted from carotid plaque samples, n ± 700. As of Thursday, March 17, 2022 all samples have been selected and RNA has been extracted; quality control (QC) was performed and we have a dataset of 635 samples.

  2. single-cell RNAseq (scRNAseq) of at least n = 40 samples (20 females, 20 males). As of Thursday, March 17, 2022 data is available of 40 samples (3 females, 15 males), we are extending sampling to get more female samples.

Plaque samples are derived from carotid endarterectomies as part of the Athero-Express Biobank Study which is an ongoing study in the UMC Utrecht.

Background

Here we map the HDAC9 to single-cells from the plaques.


library(openxlsx)

gene_list_df <- read.xlsx(paste0(PROJECT_loc, "/targets/Genes.xlsx"), sheet = "Genes")

target_genes <- unlist(gene_list_df$Gene)
target_genes
[1] "HDAC9"  "TWIST1" "IL6"    "IL1B"  

Load data

First we will load the data:

  • scRNAseq experimental data and rename the cell types.
  • Athero-Express clinical data.

Here we load the latest dataset from our Athero-Express single-cell RNA experiment.


# load(paste0(AESCRNA_loc, "/20210811.46.patients.KP.RData"))
# scRNAseqData <- seuset
# rm(seuset)
# 
# saveRDS(scRNAseqData, paste0(AESCRNA_loc, "/20210811.46.patients.KP.RDS"))

scRNAseqData <- readRDS(paste0(AESCRNA_loc, "/20210811.46.patients.KP.RDS"))

scRNAseqData
An object of class Seurat 
36147 features across 4948 samples within 2 assays 
Active assay: RNA (20111 features, 0 variable features)
 1 other assay present: SCT
 2 dimensional reductions calculated: pca, umap

The naming/classification is based on a combination conventional markers. We do not claim to know the exact identity of each cell, rather we refer to cells as ‘KIT+ Mast cells”-like cells. Likewise we refer to the cell clusters as ’communities’ of cells that exhibit similar properties, i.e. similar defining markers (e.g. KIT).

We will rename the cell types to human readable names.

### change names for clarity
backup.scRNAseqData = scRNAseqData
# get the old names to change to new names
UMAPPlot(scRNAseqData, label = FALSE, pt.size = 1.25, label.size = 4, group.by = "ident")

unique(scRNAseqData@active.ident)
 [1] CD3+ T Cells I                                 CD3+ T Cells IV                               
 [3] CD34+ Endothelial Cells I                      CD3+ T Cells V                                
 [5] CD3+CD56+ NK Cells II                          CD3+ T Cells VI                               
 [7] CD68+IL18+TLR4+TREM2+ Resident macrophages     CD3+CD56+ NK Cells I                          
 [9] ACTA2+ Smooth Muscle Cells                     CD3+ T Cells II                               
[11] FOXP3+ T Cells                                 CD34+ Endothelial Cells II                    
[13] CD3+ T Cells III                               CD68+CD1C+ Dendritic Cells                    
[15] CD68+CASP1+IL1B+SELL+ Inflammatory macrophages CD79A+ Class-switched Memory B Cells          
[17] CD68+ABCA1+OLR1+TREM2+ Foam Cells              CD68+KIT+ Mast Cells                          
[19] CD68+CD4+ Monocytes                            CD79+ Plasma B Cells                          
20 Levels: CD3+ T Cells I CD3+ T Cells II CD3+ T Cells III ... CD79+ Plasma B Cells
celltypes <- c("CD68+CD4+ Monocytes" = "CD68+CD4+ Mono", 
               "CD68+IL18+TLR4+TREM2+ Resident macrophages" = "CD68+IL18+TLR4+TREM2+ MRes", 
               "CD68+CD1C+ Dendritic Cells" = "CD68+CD1C+ DC",
               "CD68+CASP1+IL1B+SELL+ Inflammatory macrophages" = "CD68+CASP1+IL1B+SELL MInf",
               "CD68+ABCA1+OLR1+TREM2+ Foam Cells" = "CD68+ABCA1+OLR1+TREM2+ FC",
               
               # T-cells
               "CD3+ T Cells I" = "CD3+ TC I",
               "CD3+ T Cells II" = "CD3+ TC II", 
               "CD3+ T Cells III" = "CD3+ TC III", 
               "CD3+ T Cells IV" = "CD3+ TC IV", 
               "CD3+ T Cells V" = "CD3+ TC V", 
               "CD3+ T Cells VI" = "CD3+ TC VI", 
               "FOXP3+ T Cells" = "FOXP3+ TC",
               
               # Endothelial cells
               "CD34+ Endothelial Cells I" = "CD34+ EC I", 
               "CD34+ Endothelial Cells II" = "CD34+ EC II", 
               
               # SMC
               "ACTA2+ Smooth Muscle Cells" = "ACTA2+ SMC", 
               
               # NK Cells
               "CD3+CD56+ NK Cells I" = "CD3+CD56+ NK I",
               "CD3+CD56+ NK Cells II" = "CD3+CD56+ NK II",
               # Mast
               "CD68+KIT+ Mast Cells" = "CD68+KIT+ MC",
               
               "CD79A+ Class-switched Memory B Cells" = "CD79A+ BCmem", 
               "CD79+ Plasma B Cells" = "CD79+ BCplasma")

scRNAseqData <- Seurat::RenameIdents(object = scRNAseqData, 
                                       celltypes)
UMAPPlot(scRNAseqData, label = TRUE, pt.size = 1.25, label.size = 4, group.by = "ident",
         repel = TRUE)

Clinical data

Loading the Athero-Express clinical data.


AEDB.CEA <- readRDS(file = paste0(OUT_loc, "/20220317.HDAC9.AEDB.CEA.RDS"))

# Baseline table variables
basetable_vars = c("Hospital", "ORyear", "Artery_summary",
                   "Age", "Gender", 
                   # "TC_finalCU", "LDL_finalCU", "HDL_finalCU", "TG_finalCU", 
                   "TC_final", "LDL_final", "HDL_final", "TG_final", 
                   # "hsCRP_plasma",
                   "systolic", "diastoli", "GFR_MDRD", "BMI", 
                   "KDOQI", "BMI_WHO",
                   "SmokerStatus", "AlcoholUse",
                   "DiabetesStatus", 
                   "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs", 
                   "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                   "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
                   "Symptoms.Update2G", "Symptoms.Update3G", "indexsymptoms_latest_4g",
                   "restenos", "stenose",
                   "CAD_history", "PAOD", "Peripheral.interv", 
                   "EP_composite", "EP_composite_time", "EP_major", "EP_major_time",
                   "MAC_rankNorm", "SMC_rankNorm", "Macrophages.bin", "SMC.bin",
                   "Neutrophils_rankNorm", "MastCells_rankNorm",
                   "IPH.bin", "VesselDensity_rankNorm",
                   "Calc.bin", "Collagen.bin", 
                   "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype", "Plaque_Vulnerability_Index")

basetable_bin = c("Gender",  "Artery_summary",
                  "KDOQI", "BMI_WHO",
                  "SmokerStatus", "AlcoholUse",
                  "DiabetesStatus", 
                  "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs", 
                  "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                  "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
                  "Symptoms.Update2G", "Symptoms.Update3G", "indexsymptoms_latest_4g",
                  "restenos", "stenose",
                  "CAD_history", "PAOD", "Peripheral.interv", 
                  "EP_major", "EP_composite", "Macrophages.bin", "SMC.bin",
                  "IPH.bin", 
                  "Calc.bin", "Collagen.bin", 
                  "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype", "Plaque_Vulnerability_Index")
# basetable_bin

basetable_con = basetable_vars[!basetable_vars %in% basetable_bin]
# basetable_con

AESCRNA: baseline characteristics

Preparation

metadata <- scRNAseqData@meta.data %>% as_tibble() %>% separate(orig.ident, c("Patient", NA))
scRNAseqDataMeta <- metadata %>% distinct(Patient, .keep_all = TRUE)

scRNAseqDataMetaAE <- merge(scRNAseqDataMeta, AEDB.CEA, by.x = "Patient", by.y = "STUDY_NUMBER", sort = FALSE, all.x = TRUE)
dim(scRNAseqDataMetaAE)
[1]   46 1741
# Replace missing data 
# Ref: https://cran.r-project.org/web/packages/naniar/vignettes/replace-with-na.html
require(naniar)

na_strings <- c("NA", "N A", "N / A", "N/A", "N/ A", 
                "Not Available", "Not available", 
                "missing", 
                "-999", "-99", 
                "No data available/missing", "No data available/Missing")
# Then you write ~.x %in% na_strings - which reads as “does this value occur in the list of NA strings”.

scRNAseqDataMetaAE %>%
  replace_with_na_all(condition = ~.x %in% na_strings)
cat("====================================================================================================")
====================================================================================================
cat("SELECTION THE SHIZZLE")
SELECTION THE SHIZZLE
cat("- sanity checking PRIOR to selection")
- sanity checking PRIOR to selection
library(data.table)
require(labelled)
Loading required package: labelled
ae.gender <- to_factor(scRNAseqDataMetaAE$Gender)
ae.hospital <- to_factor(scRNAseqDataMetaAE$Hospital)
table(ae.gender, ae.hospital, dnn = c("Sex", "Hospital"), useNA = "ifany")
        Hospital
Sex      St. Antonius, Nieuwegein UMC Utrecht <NA>
  female                        0          18    0
  male                          0          26    0
  <NA>                          0           0    2
ae.artery <- to_factor(scRNAseqDataMetaAE$Artery_summary)
table(ae.artery, ae.gender, dnn = c("Sex", "Artery"), useNA = "ifany")
                                                                                         Artery
Sex                                                                                       female male <NA>
  No artery known (yet), no surgery (patient ill, died, exited study), re-numbered to AAA      0    0    0
  carotid (left & right)                                                                      18   25    0
  femoral/iliac (left, right or both sides)                                                    0    0    0
  other carotid arteries (common, external)                                                    0    1    0
  carotid bypass and injury (left, right or both sides)                                        0    0    0
  aneurysmata (carotid & femoral)                                                              0    0    0
  aorta                                                                                        0    0    0
  other arteries (renal, popliteal, vertebral)                                                 0    0    0
  femoral bypass, angioseal and injury (left, right or both sides)                             0    0    0
  <NA>                                                                                         0    0    2
ae.ic <- to_factor(scRNAseqDataMetaAE$informedconsent)
table(ae.ic, ae.gender, useNA = "ifany")
                                                                                                 ae.gender
ae.ic                                                                                             female male <NA>
  no, died                                                                                             0    0    0
  yes                                                                                                  9   14    0
  yes, health treatment when possible                                                                  5    7    0
  yes, no health treatment                                                                             2    2    0
  yes, no health treatment, no commercial business                                                     1    2    0
  yes, no tissue, no commerical business                                                               0    0    0
  yes, no tissue, no questionnaires, no medical info, no commercial business                           0    0    0
  yes, no questionnaires, no health treatment, no commercial business                                  0    0    0
  yes, no questionnaires, health treatment when possible                                               0    0    0
  yes, no tissue, no questionnaires, no health treatment, no commerical business                       0    0    0
  yes, no health treatment, no medical info, no commercial business                                    0    0    0
  yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business      0    0    0
  yes, no questionnaires, no health treatment                                                          0    0    0
  yes, no tissue, no health treatment                                                                  0    0    0
  yes, no tissue, no questionnaires                                                                    0    0    0
  yes, no tissue, health treatment when possible                                                       0    0    0
  yes, no tissue                                                                                       0    0    0
  yes, no commerical business                                                                          1    1    0
  yes, health treatment when possible, no commercial business                                          0    0    0
  yes, no medical info, no commercial business                                                         0    0    0
  yes, no questionnaires                                                                               0    0    0
  yes, no tissue, no questionnaires, no health treatment, no medical info                              0    0    0
  yes, no tissue, no questionnaires, no health treatment, no commercial business                       0    0    0
  yes, no medical info                                                                                 0    0    0
  yes, no questionnaires, no commercial business                                                       0    0    0
  yes, no questionnaires, no health treatment, no medical info                                         0    0    0
  yes, no questionnaires, health treatment when possible, no commercial business                       0    0    0
  yes,  no health treatment, no medical info                                                           0    0    0
  no, doesn't want to                                                                                  0    0    0
  no, unable to sign                                                                                   0    0    0
  no, no reaction                                                                                      0    0    0
  no, lost                                                                                             0    0    0
  no, too old                                                                                          0    0    0
  yes, no medical info, health treatment when possible                                                 0    0    0
  no (never asked for IC because there was no tissue)                                                  0    0    0
  yes, no medical info, no commercial business, health treatment when possible                         0    0    0
  no, endpoint                                                                                         0    0    0
  wil niets invullen, wel alles gebruiken                                                              0    0    0
  second informed concents: yes, no commercial business                                                0    0    0
  nooit geincludeerd                                                                                   0    0    0
  <NA>                                                                                                 0    0    2
rm(ae.gender, ae.hospital, ae.artery, ae.ic)


scRNAseqDataMetaAE.all <- subset(scRNAseqDataMetaAE,
                                 (Artery_summary == "carotid (left & right)" | Artery_summary == "other carotid arteries (common, external)" ) & # we only want carotids
                                   informedconsent != "missing" & # we are really strict in selecting based on 'informed consent'!
                                   informedconsent != "no, died" &
                                   informedconsent != "yes, no tissue, no commerical business" &
                                   informedconsent != "yes, no tissue, no questionnaires, no medical info, no commercial business" &
                                   informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commerical business" &
                                   informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business" &
                                   informedconsent != "yes, no tissue, no health treatment" &
                                   informedconsent != "yes, no tissue, no questionnaires" &
                                   informedconsent != "yes, no tissue, health treatment when possible" &
                                   informedconsent != "yes, no tissue" &
                                   informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info" &
                                   informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commercial business" &
                                   informedconsent != "no, doesn't want to" &
                                   informedconsent != "no, unable to sign" &
                                   informedconsent != "no, no reaction" &
                                   informedconsent != "no, lost" &
                                   informedconsent != "no, too old" &
                                   informedconsent != "yes, no medical info, health treatment when possible" & 
                                   informedconsent != "no (never asked for IC because there was no tissue)" &
                                   informedconsent != "no, endpoint" &
                                   informedconsent != "nooit geincludeerd" & 
                                   informedconsent != "yes, no health treatment, no commercial business" & # IMPORTANT: since we are sharing with a commercial party
                                   informedconsent != "yes, no tissue, no commerical business" & 
                                   informedconsent != "yes, no tissue, no questionnaires, no medical info, no commercial business" & 
                                   informedconsent != "yes, no questionnaires, no health treatment, no commercial business" & 
                                   informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commerical business" & 
                                   informedconsent != "yes, no health treatment, no medical info, no commercial business" & 
                                   informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business" & 
                                   informedconsent != "yes, no commerical business" & 
                                   informedconsent != "yes, health treatment when possible, no commercial business" & 
                                   informedconsent != "yes, no medical info, no commercial business" & 
                                   informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commercial business" & 
                                   informedconsent != "yes, no questionnaires, no commercial business" & 
                                   informedconsent != "yes, no questionnaires, health treatment when possible, no commercial business" & 
                                   informedconsent != "second informed concents: yes, no commercial business")
# scRNAseqDataMetaAE.all[1:10, 1:10]
dim(scRNAseqDataMetaAE.all)
[1]   39 1741
# DT::datatable(scRNAseqDataMetaAE.all)

Baseline

Showing the baseline table for the scRNAseq data in 39 CEA patients with informed consent.

cat("===========================================================================================")
===========================================================================================
cat("CREATE BASELINE TABLE")
CREATE BASELINE TABLE
# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
scRNAseqDataMetaAE.all.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                                  # factorVars = basetable_bin,
                                                  # strata = "Gender",
                                                  data = scRNAseqDataMetaAE.all, includeNA = TRUE), 
                                   nonnormal = c(), 
                                   quote = FALSE, showAllLevels = TRUE,
                                   format = "p", 
                                   contDigits = 3)[,1:2]
Warning in ModuleReturnVarsExist(vars, data) :
  These variables only have NA/NaN: MAC_rankNorm SMC_rankNorm Neutrophils_rankNorm MastCells_rankNorm IPH.bin VesselDensity_rankNorm  Dropped
                                     
                                      level                                                                                  
  n                                                                                                                          
  Hospital (%)                        St. Antonius, Nieuwegein                                                               
                                      UMC Utrecht                                                                            
  ORyear (%)                          2002                                                                                   
                                      2003                                                                                   
                                      2004                                                                                   
                                      2005                                                                                   
                                      2006                                                                                   
                                      2007                                                                                   
                                      2008                                                                                   
                                      2009                                                                                   
                                      2010                                                                                   
                                      2011                                                                                   
                                      2012                                                                                   
                                      2013                                                                                   
                                      2014                                                                                   
                                      2015                                                                                   
                                      2016                                                                                   
                                      2017                                                                                   
                                      2018                                                                                   
                                      2019                                                                                   
                                      2020                                                                                   
                                      2021                                                                                   
                                      2022                                                                                   
  Artery_summary (%)                  No artery known (yet), no surgery (patient ill, died, exited study), re-numbered to AAA
                                      carotid (left & right)                                                                 
                                      femoral/iliac (left, right or both sides)                                              
                                      other carotid arteries (common, external)                                              
                                      carotid bypass and injury (left, right or both sides)                                  
                                      aneurysmata (carotid & femoral)                                                        
                                      aorta                                                                                  
                                      other arteries (renal, popliteal, vertebral)                                           
                                      femoral bypass, angioseal and injury (left, right or both sides)                       
  Age (mean (SD))                                                                                                            
  Gender (%)                          female                                                                                 
                                      male                                                                                   
  TC_final (mean (SD))                                                                                                       
  LDL_final (mean (SD))                                                                                                      
  HDL_final (mean (SD))                                                                                                      
  TG_final (mean (SD))                                                                                                       
  systolic (mean (SD))                                                                                                       
  diastoli (mean (SD))                                                                                                       
  GFR_MDRD (mean (SD))                                                                                                       
  BMI (mean (SD))                                                                                                            
  KDOQI (%)                           Normal kidney function                                                                 
                                      CKD 2 (Mild)                                                                           
                                      CKD 3 (Moderate)                                                                       
                                      CKD 4 (Severe)                                                                         
                                      CKD 5 (Failure)                                                                        
                                      <NA>                                                                                   
  BMI_WHO (%)                         Underweight                                                                            
                                      Normal                                                                                 
                                      Overweight                                                                             
                                      Obese                                                                                  
                                      <NA>                                                                                   
  SmokerStatus (%)                    Current smoker                                                                         
                                      Ex-smoker                                                                              
                                      Never smoked                                                                           
                                      <NA>                                                                                   
  AlcoholUse (%)                      Yes                                                                                    
                                      <NA>                                                                                   
  DiabetesStatus (%)                  Diabetes                                                                               
                                      <NA>                                                                                   
  Hypertension.selfreport (%)         no                                                                                     
                                      yes                                                                                    
                                      <NA>                                                                                   
  Hypertension.selfreportdrug (%)     no                                                                                     
                                      yes                                                                                    
                                      <NA>                                                                                   
  Hypertension.composite (%)          no                                                                                     
                                      yes                                                                                    
  Hypertension.drugs (%)              no                                                                                     
                                      yes                                                                                    
                                      <NA>                                                                                   
  Med.anticoagulants (%)              no                                                                                     
                                      yes                                                                                    
                                      <NA>                                                                                   
  Med.all.antiplatelet (%)            no                                                                                     
                                      yes                                                                                    
                                      <NA>                                                                                   
  Med.Statin.LLD (%)                  no                                                                                     
                                      yes                                                                                    
                                      <NA>                                                                                   
  Stroke_Dx (%)                       No stroke diagnosed                                                                    
                                      Stroke diagnosed                                                                       
  sympt (%)                           Asymptomatic                                                                           
                                      TIA                                                                                    
                                      minor stroke                                                                           
                                      Major stroke                                                                           
                                      Amaurosis fugax                                                                        
                                      Four vessel disease                                                                    
                                      Vertebrobasilary TIA                                                                   
                                      Retinal infarction                                                                     
                                      Symptomatic, but aspecific symtoms                                                     
                                      Contralateral symptomatic occlusion                                                    
                                      retinal infarction                                                                     
                                      armclaudication due to occlusion subclavian artery, CEA needed for bypass              
                                      retinal infarction + TIAs                                                              
                                      Ocular ischemic syndrome                                                               
                                      ischemisch glaucoom                                                                    
                                      subclavian steal syndrome                                                              
                                      TGA                                                                                    
  Symptoms.5G (%)                     Ocular                                                                                 
                                      Other                                                                                  
                                      Retinal infarction                                                                     
                                      Stroke                                                                                 
                                      TIA                                                                                    
  AsymptSympt (%)                     Ocular and others                                                                      
                                      Symptomatic                                                                            
  AsymptSympt2G (%)                   Symptomatic                                                                            
  Symptoms.Update2G (%)               Symptomatic                                                                            
                                      <NA>                                                                                   
  Symptoms.Update3G (%)               Symptomatic                                                                            
                                      <NA>                                                                                   
  indexsymptoms_latest_4g (mean (SD))                                                                                        
  restenos (%)                        de novo                                                                                
                                      restenosis                                                                             
                                      stenose bij angioseal na PTCA                                                          
  stenose (%)                         0-49%                                                                                  
                                      50-70%                                                                                 
                                      70-90%                                                                                 
                                      90-99%                                                                                 
                                      100% (Occlusion)                                                                       
                                      NA                                                                                     
                                      50-99%                                                                                 
                                      70-99%                                                                                 
                                      99                                                                                     
  CAD_history (%)                     No history CAD                                                                         
                                      History CAD                                                                            
  PAOD (%)                            no                                                                                     
                                      yes                                                                                    
  Peripheral.interv (%)               no                                                                                     
                                      yes                                                                                    
  EP_composite (%)                    No composite endpoints                                                                 
                                      Composite endpoints                                                                    
                                      <NA>                                                                                   
  EP_composite_time (mean (SD))                                                                                              
  EP_major (%)                        No major events (endpoints)                                                            
                                      Major events (endpoints)                                                               
                                      <NA>                                                                                   
  EP_major_time (mean (SD))                                                                                                  
  Macrophages.bin (%)                 no/minor                                                                               
                                      moderate/heavy                                                                         
                                      <NA>                                                                                   
  SMC.bin (%)                         no/minor                                                                               
                                      moderate/heavy                                                                         
                                      <NA>                                                                                   
  Calc.bin (%)                        no/minor                                                                               
                                      moderate/heavy                                                                         
                                      <NA>                                                                                   
  Collagen.bin (%)                    no/minor                                                                               
                                      moderate/heavy                                                                         
                                      <NA>                                                                                   
  Fat.bin_10 (%)                       <10%                                                                                  
                                       >10%                                                                                  
                                      <NA>                                                                                   
  Fat.bin_40 (%)                      <40%                                                                                   
                                      >40%                                                                                   
                                      <NA>                                                                                   
  OverallPlaquePhenotype (%)          atheromatous                                                                           
                                      fibroatheromatous                                                                      
                                      fibrous                                                                                
                                      <NA>                                                                                   
  Plaque_Vulnerability_Index (%)      0                                                                                      
                                      1                                                                                      
                                      2                                                                                      
                                      3                                                                                      
                                      4                                                                                      
                                      5                                                                                      
                                     
                                      Overall           
  n                                         39          
  Hospital (%)                             0.0          
                                         100.0          
  ORyear (%)                               0.0          
                                           0.0          
                                           0.0          
                                           0.0          
                                           0.0          
                                           0.0          
                                           0.0          
                                           0.0          
                                           0.0          
                                           0.0          
                                           0.0          
                                           0.0          
                                           0.0          
                                           0.0          
                                           0.0          
                                           0.0          
                                          51.3          
                                          35.9          
                                          10.3          
                                           2.6          
                                           0.0          
  Artery_summary (%)                       0.0          
                                          97.4          
                                           0.0          
                                           2.6          
                                           0.0          
                                           0.0          
                                           0.0          
                                           0.0          
                                           0.0          
  Age (mean (SD))                       38.077 (8.183)  
  Gender (%)                              41.0          
                                          59.0          
  TC_final (mean (SD))                   4.533 (1.252)  
  LDL_final (mean (SD))                  2.676 (1.013)  
  HDL_final (mean (SD))                  1.135 (0.229)  
  TG_final (mean (SD))                   1.927 (1.093)  
  systolic (mean (SD))                  61.842 (27.013) 
  diastoli (mean (SD))                  40.658 (16.285) 
  GFR_MDRD (mean (SD))                1386.429 (814.340)
  BMI (mean (SD))                      653.250 (324.553)
  KDOQI (%)                               28.2          
                                          33.3          
                                          28.2          
                                           0.0          
                                           0.0          
                                          10.3          
  BMI_WHO (%)                              2.6          
                                          33.3          
                                          38.5          
                                          17.9          
                                           7.7          
  SmokerStatus (%)                        28.2          
                                          53.8          
                                          12.8          
                                           5.1          
  AlcoholUse (%)                          38.5          
                                          61.5          
  DiabetesStatus (%)                      71.8          
                                          28.2          
  Hypertension.selfreport (%)              7.7          
                                          87.2          
                                           5.1          
  Hypertension.selfreportdrug (%)          7.7          
                                          87.2          
                                           5.1          
  Hypertension.composite (%)               7.7          
                                          92.3          
  Hypertension.drugs (%)                  10.3          
                                          84.6          
                                           5.1          
  Med.anticoagulants (%)                  87.2          
                                           5.1          
                                           7.7          
  Med.all.antiplatelet (%)                20.5          
                                          74.4          
                                           5.1          
  Med.Statin.LLD (%)                      20.5          
                                          74.4          
                                           5.1          
  Stroke_Dx (%)                           56.4          
                                          43.6          
  sympt (%)                               15.4          
                                          17.9          
                                          25.6          
                                          10.3          
                                          15.4          
                                           0.0          
                                           0.0          
                                           2.6          
                                           2.6          
                                           0.0          
                                           2.6          
                                           0.0          
                                           0.0          
                                           7.7          
                                           0.0          
                                           0.0          
                                           0.0          
  Symptoms.5G (%)                         17.9          
                                          17.9          
                                           5.1          
                                          43.6          
                                          15.4          
  AsymptSympt (%)                         41.0          
                                          59.0          
  AsymptSympt2G (%)                      100.0          
  Symptoms.Update2G (%)                   64.1          
                                          35.9          
  Symptoms.Update3G (%)                   64.1          
                                          35.9          
  indexsymptoms_latest_4g (mean (SD))    2.769 (1.111)  
  restenos (%)                           100.0          
                                           0.0          
                                           0.0          
  stenose (%)                              2.6          
                                          10.3          
                                          46.2          
                                          25.6          
                                           0.0          
                                           0.0          
                                           0.0          
                                          15.4          
                                           0.0          
  CAD_history (%)                         79.5          
                                          20.5          
  PAOD (%)                                84.6          
                                          15.4          
  Peripheral.interv (%)                   76.9          
                                          23.1          
  EP_composite (%)                        69.2          
                                          10.3          
                                          20.5          
  EP_composite_time (mean (SD))          1.878 (1.026)  
  EP_major (%)                            71.8          
                                           7.7          
                                          20.5          
  EP_major_time (mean (SD))              1.957 (1.031)  
  Macrophages.bin (%)                      2.6          
                                           0.0          
                                          97.4          
  SMC.bin (%)                              0.0          
                                           2.6          
                                          97.4          
  Calc.bin (%)                             2.6          
                                           0.0          
                                          97.4          
  Collagen.bin (%)                         0.0          
                                           2.6          
                                          97.4          
  Fat.bin_10 (%)                           0.0          
                                           2.6          
                                          97.4          
  Fat.bin_40 (%)                           2.6          
                                           0.0          
                                          97.4          
  OverallPlaquePhenotype (%)               2.6          
                                           0.0          
                                           0.0          
                                          97.4          
  Plaque_Vulnerability_Index (%)          97.4          
                                           2.6          
                                           0.0          
                                           0.0          
                                           0.0          
                                           0.0          

Writing the baseline table to Excel format.

# Write basetable
require(openxlsx)
# write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AESCRNA.CEA.39pts.after_qc.IC_commercial.BaselineTable.xlsx"), 
#            format(scRNAseqDataMetaAE.all.tableOne, digits = 5, scientific = FALSE) , 
#            rowNames = TRUE, colNames = TRUE, overwrite = TRUE)

write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AESCRNA.CEA.39pts.after_qc.IC_academic.BaselineTable.xlsx"), 
           format(scRNAseqDataMetaAE.all.tableOne, digits = 5, scientific = FALSE) , 
           rowNames = TRUE, colNames = TRUE, overwrite = TRUE)

AESCRNA

Quality control

Here review the number of cells per sample, plate, and patients. And plot the ratio’s per sample and study number.

## check stuff
cat("\nHow many cells per type ...?")

How many cells per type ...?
sort(table(scRNAseqData@meta.data$SCT_snn_res.0.8))
integer(0)
# cat("\n\nHow many cells per plate ...?")
# sort(table(scRNAseqData@meta.data$ID))

# cat("\n\nHow many cells per type per plate ...?")
# table(scRNAseqData@meta.data$SCT_snn_res.0.8, scRNAseqData@meta.data$ID)

cat("\n\nHow many cells per patient ...?")


How many cells per patient ...?
sort(table(scRNAseqData@meta.data$Patient))

4530 4675 4440 4605 4653 4472 4458 4455 4476 4587 4496 4601 4502 4501 4571 4478 4448 4477 4452 4459 4520 4602 4489 4432 4495 4545 4558 4480 4447 
   3    4    6    7   20   22   35   54   59   60   70   70   73   75   76   77   80   84   92   94   96   96   97   99  102  106  107  112  114 
4500 4513 4535 4676 4486 4470 4487 4546 4488 4521 4580 4491 4541 4450 4542 4453 4443 
 116  123  130  135  137  144  144  144  146  161  163  175  178  205  213  222  422 
cat("\n\nVisualizing these ratio's per study number and sample ...?")


Visualizing these ratio's per study number and sample ...?
UMAPPlot(scRNAseqData, label = TRUE, pt.size = 1.25, label.size = 4, group.by = "ident",
         repel = TRUE)

ggsave(paste0(PLOT_loc, "/", Today, ".UMAP.png"), plot = last_plot())
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ggsave(paste0(PLOT_loc, "/", Today, ".UMAP.ps"), plot = last_plot())
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# barplot(prop.table(x = table(scRNAseqData@active.ident, scRNAseqData@meta.data$Patient)), 
#         cex.axis = 1.0, cex.names = 0.5, las = 1,
#         col = uithof_color, xlab = "study number", legend.text = FALSE, args.legend = list(x = "bottom"))
# dev.copy(pdf, paste0(QC_loc, "/", Today, ".cell_ratios_per_sample.pdf"))
# dev.off()

# barplot(prop.table(x = table(scRNAseqData@active.ident, scRNAseqData@meta.data$ID)), 
#         cex.axis = 1.0, cex.names = 0.5, las = 2,
#         col = uithof_color, xlab = "sample ID", legend.text = FALSE, args.legend = list(x = "bottom"))
# dev.copy(pdf, paste0(QC_loc, "/", Today, ".cell_ratios_per_sample_per_plate.pdf"))
# dev.off()

Visualisations

Let’s project known cellular markers.


UMAPPlot(scRNAseqData, label = FALSE, pt.size = 1.25, label.size = 4, group.by = "ident",
         repel = TRUE)


# endothelial cells
FeaturePlot(scRNAseqData, features = c("CD34"), cols =  c("#ECECEC", "#DB003F"))

FeaturePlot(scRNAseqData, features = c("EDN1"), cols =  c("#ECECEC", "#DB003F"))

FeaturePlot(scRNAseqData, features = c("EDNRA", "EDNRB"), cols =  c("#ECECEC", "#DB003F"))

FeaturePlot(scRNAseqData, features = c("CDH5", "PECAM1"), cols =  c("#ECECEC", "#DB003F"))

FeaturePlot(scRNAseqData, features = c("ACKR1"), cols =  c("#ECECEC", "#DB003F"))


# SMC
FeaturePlot(scRNAseqData, features = c("MYH11"), cols =  c("#ECECEC", "#DB003F"))

FeaturePlot(scRNAseqData, features = c("LGALS3", "ACTA2"), cols =  c("#ECECEC", "#DB003F"))


# macrophages
FeaturePlot(scRNAseqData, features = c("CD14", "CD68"), cols =  c("#ECECEC", "#DB003F"))

FeaturePlot(scRNAseqData, features = c("CD36"), cols =  c("#ECECEC", "#DB003F"))


# t-cells
FeaturePlot(scRNAseqData, features = c("CD3E"), cols =  c("#ECECEC", "#DB003F"))

FeaturePlot(scRNAseqData, features = c("CD4"), cols =  c("#ECECEC", "#DB003F"))

# FeaturePlot(scRNAseqData, features = c("CD8"), cols =  c("#ECECEC", "#DB003F"))

# b-cells
FeaturePlot(scRNAseqData, features = c("CD79A"), cols =  c("#ECECEC", "#DB003F"))


# mast cells
FeaturePlot(scRNAseqData, features = c("KIT"), cols =  c("#ECECEC", "#DB003F"))


# NK cells
FeaturePlot(scRNAseqData, features = c("NCAM1"), cols =  c("#ECECEC", "#DB003F"))

Targets of interest:

We check whether the targets genes were sequenced using our method.

length(target_genes)
[1] 4
target_genes
[1] "HDAC9"  "TWIST1" "IL6"    "IL1B"  

Expression in cell communities


# target_genes_rm <- c("AC011294.3", "C6orf195", "C9orf53", "AL137026.1", "RP11-145E5.5",
#                      "ZNF32", "BCAM", "DUPD1", "PVRL2")
# 
# temp = target_genes[!target_genes %in% target_genes_rm]
# 
# target_genes_qc <- c(temp, "DUSP27", "NECTIN2")

target_genes_qc <- target_genes
target_genes_qc
[1] "HDAC9"  "TWIST1" "IL6"    "IL1B"  
library(RColorBrewer)

p1 <- DotPlot(scRNAseqData, features = target_genes_qc,
        cols = "RdBu")

p1 + theme(axis.text.x = element_text(angle = 45, hjust=1, size = 5))


ggsave(paste0(PLOT_loc, "/", Today, ".DotPlot.Targets.png"), plot = last_plot())
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ggsave(paste0(PLOT_loc, "/", Today, ".DotPlot.Targets.ps"), plot = last_plot())
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ggsave(paste0(PLOT_loc, "/", Today, ".DotPlot.Targets.pdf"), plot = last_plot())
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rm(p1)

# FeaturePlot(scRNAseqData, features = c(target_genes_qc),
#             cols =  c("#ECECEC", "#DB003F", "#9A3480","#1290D9"),
#             combine = TRUE)
# 
# ggsave(paste0(PLOT_loc, "/", Today, ".FeaturePlot.Targets.png"), plot = last_plot())
# ggsave(paste0(PLOT_loc, "/", Today, ".FeaturePlot.Targets.ps"), plot = last_plot())
# VlnPlot(scRNAseqData, features = "DUSP27")

# VlnPlot files
ifelse(!dir.exists(file.path(PLOT_loc, "/VlnPlot")), 
       dir.create(file.path(PLOT_loc, "/VlnPlot")), 
       FALSE)
[1] TRUE
VlnPlot_loc = paste0(PLOT_loc, "/VlnPlot")


for (GENE in target_genes_qc){
  print(paste0("Projecting the expression of ", GENE, "."))

  vp1 <-  VlnPlot(scRNAseqData, features = GENE) + 
    xlab("cell communities") + 
    ylab(bquote("normalized expression")) +
    theme(axis.title.x = element_text(color = "#000000", size = 14, face = "bold"), 
            axis.title.y = element_text(color = "#000000", size = 14, face = "bold"), 
            legend.position = "none")
    ggsave(paste0(VlnPlot_loc, "/", Today, ".VlnPlot.",GENE,".png"), plot = last_plot())
    ggsave(paste0(VlnPlot_loc, "/", Today, ".VlnPlot.",GENE,".ps"), plot = last_plot())
    ggsave(paste0(VlnPlot_loc, "/", Today, ".VlnPlot.",GENE,".pdf"), plot = last_plot())
  
  # print(vp1)
  
}
[1] "Projecting the expression of HDAC9."
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[1] "Projecting the expression of TWIST1."
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[1] "Projecting the expression of IL6."
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[1] "Projecting the expression of IL1B."
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Differential expression between cell communities

Here we project genes to only the broad cell communities:

  • macrophages
  • endothelial cells
  • smooth muscle cells
  • T-cells
  • B-cells
  • Mast cells
  • NK-cells
  • Mixed cells

Macrophages

unique(scRNAseqData@active.ident)
 [1] CD3+ TC I                  CD3+ TC IV                 CD34+ EC I                 CD3+ TC V                  CD3+CD56+ NK II           
 [6] CD3+ TC VI                 CD68+IL18+TLR4+TREM2+ MRes CD3+CD56+ NK I             ACTA2+ SMC                 CD3+ TC II                
[11] FOXP3+ TC                  CD34+ EC II                CD3+ TC III                CD68+CD1C+ DC              CD68+CASP1+IL1B+SELL MInf 
[16] CD79A+ BCmem               CD68+ABCA1+OLR1+TREM2+ FC  CD68+KIT+ MC               CD68+CD4+ Mono             CD79+ BCplasma            
20 Levels: CD68+CD4+ Mono CD68+IL18+TLR4+TREM2+ MRes CD68+CD1C+ DC CD68+CASP1+IL1B+SELL MInf CD68+ABCA1+OLR1+TREM2+ FC CD3+ TC I ... CD79+ BCplasma

Comparison between the macrophages cell communities (CD14/CD68+), and all other communities.


MAC.markers <- FindMarkers(object = scRNAseqData, 
                          ident.1 = c("CD68+CASP1+IL1B+SELL MInf", 
                                      "CD68+CD1C+ DC", 
                                      "CD68+CD4+ Mono",
                                      "CD68+IL18+TLR4+TREM2+ MRes",
                                      "CD68+ABCA1+OLR1+TREM2+ FC"), 
                          ident.2 = c(#"CD68+CASP1+IL1B+SELL MInf", 
                                      #"CD68+CD1C+ DC", 
                                      #"CD68+CD4+ Mono",
                                      #"CD68+IL18+TLR4+TREM2+ MRes",
                                      #"CD68+ABCA1+OLR1+TREM2+ FC",
                                      "CD3+ TC I",
                                      "CD3+ TC II", 
                                      "CD3+ TC III", 
                                      "CD3+ TC IV", 
                                      "CD3+ TC V", 
                                      "CD3+ TC VI", 
                                      "FOXP3+ TC", 
                                      "CD34+ EC I", 
                                      "CD34+ EC II",
                                      "ACTA2+ SMC", 
                                      "CD3+CD56+ NK I",
                                      "CD3+CD56+ NK II", 
                                      "CD68+KIT+ MC",
                                      "CD79+ BCplasma", 
                                      "CD79A+ BCmem"))
For a more efficient implementation of the Wilcoxon Rank Sum Test,
(default method for FindMarkers) please install the limma package
--------------------------------------------
install.packages('BiocManager')
BiocManager::install('limma')
--------------------------------------------
After installation of limma, Seurat will automatically use the more 
efficient implementation (no further action necessary).
This message will be shown once per session

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~03m 07s      
  |++                                                | 2 % ~02m 37s      
  |++                                                | 3 % ~02m 25s      
  |+++                                               | 4 % ~02m 24s      
  |+++                                               | 5 % ~02m 29s      
  |++++                                              | 6 % ~02m 34s      
  |++++                                              | 7 % ~02m 30s      
  |+++++                                             | 8 % ~02m 30s      
  |+++++                                             | 9 % ~02m 29s      
  |++++++                                            | 10% ~02m 24s      
  |++++++                                            | 11% ~02m 18s      
  |+++++++                                           | 12% ~02m 13s      
  |+++++++                                           | 13% ~02m 10s      
  |++++++++                                          | 14% ~02m 06s      
  |++++++++                                          | 15% ~02m 03s      
  |+++++++++                                         | 16% ~02m 01s      
  |+++++++++                                         | 17% ~01m 59s      
  |++++++++++                                        | 18% ~01m 57s      
  |++++++++++                                        | 19% ~01m 54s      
  |+++++++++++                                       | 20% ~01m 52s      
  |+++++++++++                                       | 21% ~01m 50s      
  |++++++++++++                                      | 22% ~01m 48s      
  |++++++++++++                                      | 23% ~01m 46s      
  |+++++++++++++                                     | 24% ~01m 44s      
  |+++++++++++++                                     | 25% ~01m 42s      
  |++++++++++++++                                    | 26% ~01m 40s      
  |++++++++++++++                                    | 27% ~01m 38s      
  |+++++++++++++++                                   | 28% ~01m 36s      
  |+++++++++++++++                                   | 29% ~01m 34s      
  |++++++++++++++++                                  | 30% ~01m 32s      
  |++++++++++++++++                                  | 31% ~01m 33s      
  |+++++++++++++++++                                 | 32% ~01m 31s      
  |+++++++++++++++++                                 | 33% ~01m 30s      
  |++++++++++++++++++                                | 34% ~01m 28s      
  |++++++++++++++++++                                | 35% ~01m 26s      
  |+++++++++++++++++++                               | 36% ~01m 24s      
  |+++++++++++++++++++                               | 37% ~01m 22s      
  |++++++++++++++++++++                              | 38% ~01m 21s      
  |++++++++++++++++++++                              | 39% ~01m 19s      
  |+++++++++++++++++++++                             | 40% ~01m 17s      
  |+++++++++++++++++++++                             | 41% ~01m 16s      
  |++++++++++++++++++++++                            | 42% ~01m 14s      
  |++++++++++++++++++++++                            | 43% ~01m 13s      
  |+++++++++++++++++++++++                           | 44% ~01m 11s      
  |+++++++++++++++++++++++                           | 45% ~01m 10s      
  |++++++++++++++++++++++++                          | 46% ~01m 08s      
  |++++++++++++++++++++++++                          | 47% ~01m 07s      
  |+++++++++++++++++++++++++                         | 48% ~01m 05s      
  |+++++++++++++++++++++++++                         | 49% ~01m 04s      
  |++++++++++++++++++++++++++                        | 51% ~01m 03s      
  |++++++++++++++++++++++++++                        | 52% ~01m 01s      
  |+++++++++++++++++++++++++++                       | 53% ~01m 00s      
  |+++++++++++++++++++++++++++                       | 54% ~59s          
  |++++++++++++++++++++++++++++                      | 55% ~58s          
  |++++++++++++++++++++++++++++                      | 56% ~56s          
  |+++++++++++++++++++++++++++++                     | 57% ~55s          
  |+++++++++++++++++++++++++++++                     | 58% ~54s          
  |++++++++++++++++++++++++++++++                    | 59% ~52s          
  |++++++++++++++++++++++++++++++                    | 60% ~51s          
  |+++++++++++++++++++++++++++++++                   | 61% ~50s          
  |+++++++++++++++++++++++++++++++                   | 62% ~48s          
  |++++++++++++++++++++++++++++++++                  | 63% ~47s          
  |++++++++++++++++++++++++++++++++                  | 64% ~46s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~45s          
  |+++++++++++++++++++++++++++++++++                 | 66% ~43s          
  |++++++++++++++++++++++++++++++++++                | 67% ~42s          
  |++++++++++++++++++++++++++++++++++                | 68% ~41s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~39s          
  |+++++++++++++++++++++++++++++++++++               | 70% ~38s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~37s          
  |++++++++++++++++++++++++++++++++++++              | 72% ~35s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~34s          
  |+++++++++++++++++++++++++++++++++++++             | 74% ~33s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~32s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~30s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~29s          
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~28s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~26s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~25s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~24s          
  |+++++++++++++++++++++++++++++++++++++++++         | 82% ~23s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~21s          
  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~20s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~19s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~17s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~16s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~15s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~14s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~12s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~11s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~10s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~09s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~07s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~06s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~05s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~04s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02m 01s
DT::datatable(MAC.markers)
MAC_Volcano_TargetsA = EnhancedVolcano(MAC.markers,
    lab = rownames(MAC.markers),
    x = "avg_log2FC",
    y = "p_val_adj",
    selectLab = target_genes_qc,
    axisLabSize = 12,
    xlab = "average fold-change",
    title = "Macrophage markers\n(Macrophage communities vs the rest)",
    titleLabSize = 14,
    pCutoff = 0.05/(nrow(MAC.markers)), # 20552 genes
    FCcutoff = 1.25,
    pointSize = 1.5,
    labSize = 3.0,
    legendLabels =c('NS','avg. fold-change','P',
      'P & avg. fold-change'),
    legendPosition = "right",
    legendLabSize = 10,
    legendIconSize = 3.0,
    drawConnectors = TRUE,
    widthConnectors = 0.2,
    colConnectors = "#595A5C",
    gridlines.major = FALSE,
    gridlines.minor = FALSE)
MAC_Volcano_TargetsA

ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.MAC.DEG.Targets.pdf"), 
       plot = MAC_Volcano_TargetsA)
Saving 18 x 12 in image

The target results are given below and written to a file.

library(tibble)
MAC.markers <- add_column(MAC.markers, Gene = row.names(MAC.markers), .before = 1)

temp <- MAC.markers[MAC.markers$Gene %in% target_genes_qc,]

DT::datatable(temp)
fwrite(temp, file = paste0(OUT_loc, "/", Today, ".MAC.DEG.Targets.txt"),
       quote = FALSE,
       sep = "\t", 
       showProgress = FALSE, verbose = FALSE)

Smooth muscle cells

Comparison between the smooth muscle cell communities (ACTA2+), and all other communities.


SMC.markers <- FindMarkers(object = scRNAseqData, 
                          ident.1 = c("ACTA2+ SMC"), 
                          ident.2 = c("CD68+CASP1+IL1B+SELL MInf", 
                                      "CD68+CD1C+ DC", 
                                      "CD68+CD4+ Mono",
                                      "CD68+IL18+TLR4+TREM2+ MRes",
                                      "CD68+ABCA1+OLR1+TREM2+ FC",
                                      "CD3+ TC I",
                                      "CD3+ TC II", 
                                      "CD3+ TC III", 
                                      "CD3+ TC IV", 
                                      "CD3+ TC V", 
                                      "CD3+ TC VI", 
                                      "FOXP3+ TC", 
                                      "CD34+ EC I", 
                                      "CD34+ EC II",
                                      #"ACTA2+ SMC", 
                                      "CD3+CD56+ NK I",
                                      "CD3+CD56+ NK II", 
                                      "CD68+KIT+ MC",
                                      "CD79+ BCplasma", 
                                      "CD79A+ BCmem"))

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~02m 35s      
  |++                                                | 2 % ~02m 23s      
  |++                                                | 3 % ~02m 24s      
  |+++                                               | 4 % ~02m 18s      
  |+++                                               | 5 % ~02m 17s      
  |++++                                              | 6 % ~02m 19s      
  |++++                                              | 7 % ~02m 25s      
  |+++++                                             | 8 % ~02m 22s      
  |+++++                                             | 9 % ~02m 23s      
  |++++++                                            | 10% ~02m 22s      
  |++++++                                            | 11% ~02m 21s      
  |+++++++                                           | 12% ~02m 21s      
  |+++++++                                           | 13% ~02m 19s      
  |++++++++                                          | 14% ~02m 16s      
  |++++++++                                          | 15% ~02m 14s      
  |+++++++++                                         | 16% ~02m 12s      
  |+++++++++                                         | 17% ~02m 11s      
  |++++++++++                                        | 18% ~02m 09s      
  |++++++++++                                        | 19% ~02m 08s      
  |+++++++++++                                       | 20% ~02m 06s      
  |+++++++++++                                       | 21% ~02m 04s      
  |++++++++++++                                      | 22% ~02m 03s      
  |++++++++++++                                      | 23% ~02m 01s      
  |+++++++++++++                                     | 24% ~01m 59s      
  |+++++++++++++                                     | 26% ~01m 57s      
  |++++++++++++++                                    | 27% ~01m 55s      
  |++++++++++++++                                    | 28% ~01m 53s      
  |+++++++++++++++                                   | 29% ~01m 51s      
  |+++++++++++++++                                   | 30% ~01m 49s      
  |++++++++++++++++                                  | 31% ~01m 47s      
  |++++++++++++++++                                  | 32% ~01m 46s      
  |+++++++++++++++++                                 | 33% ~01m 44s      
  |+++++++++++++++++                                 | 34% ~01m 43s      
  |++++++++++++++++++                                | 35% ~01m 41s      
  |++++++++++++++++++                                | 36% ~01m 39s      
  |+++++++++++++++++++                               | 37% ~01m 37s      
  |+++++++++++++++++++                               | 38% ~01m 36s      
  |++++++++++++++++++++                              | 39% ~01m 35s      
  |++++++++++++++++++++                              | 40% ~01m 34s      
  |+++++++++++++++++++++                             | 41% ~01m 32s      
  |+++++++++++++++++++++                             | 42% ~01m 30s      
  |++++++++++++++++++++++                            | 43% ~01m 29s      
  |++++++++++++++++++++++                            | 44% ~01m 32s      
  |+++++++++++++++++++++++                           | 45% ~01m 30s      
  |+++++++++++++++++++++++                           | 46% ~01m 30s      
  |++++++++++++++++++++++++                          | 47% ~01m 27s      
  |++++++++++++++++++++++++                          | 48% ~01m 26s      
  |+++++++++++++++++++++++++                         | 49% ~01m 24s      
  |+++++++++++++++++++++++++                         | 50% ~01m 22s      
  |++++++++++++++++++++++++++                        | 51% ~01m 20s      
  |+++++++++++++++++++++++++++                       | 52% ~01m 18s      
  |+++++++++++++++++++++++++++                       | 53% ~01m 16s      
  |++++++++++++++++++++++++++++                      | 54% ~01m 15s      
  |++++++++++++++++++++++++++++                      | 55% ~01m 13s      
  |+++++++++++++++++++++++++++++                     | 56% ~01m 11s      
  |+++++++++++++++++++++++++++++                     | 57% ~01m 09s      
  |++++++++++++++++++++++++++++++                    | 58% ~01m 07s      
  |++++++++++++++++++++++++++++++                    | 59% ~01m 06s      
  |+++++++++++++++++++++++++++++++                   | 60% ~01m 04s      
  |+++++++++++++++++++++++++++++++                   | 61% ~01m 04s      
  |++++++++++++++++++++++++++++++++                  | 62% ~01m 02s      
  |++++++++++++++++++++++++++++++++                  | 63% ~60s          
  |+++++++++++++++++++++++++++++++++                 | 64% ~58s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~56s          
  |++++++++++++++++++++++++++++++++++                | 66% ~54s          
  |++++++++++++++++++++++++++++++++++                | 67% ~52s          
  |+++++++++++++++++++++++++++++++++++               | 68% ~50s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~49s          
  |++++++++++++++++++++++++++++++++++++              | 70% ~47s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~45s          
  |+++++++++++++++++++++++++++++++++++++             | 72% ~43s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~42s          
  |++++++++++++++++++++++++++++++++++++++            | 74% ~40s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~38s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~37s          
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~35s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~37s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~35s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~35s          
  |+++++++++++++++++++++++++++++++++++++++++         | 82% ~33s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~31s          
  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~29s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~27s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~26s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~24s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~22s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~20s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~18s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~16s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~14s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~12s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~11s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~09s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~07s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~05s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~03s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02m 49s
DT::datatable(SMC.markers)
SMC_Volcano_TargetsA = EnhancedVolcano(SMC.markers,
    lab = rownames(SMC.markers),
    x = "avg_log2FC",
    y = "p_val_adj",
    selectLab = target_genes_qc,
    axisLabSize = 12,
    xlab = "average fold-change",
    title = "SMC markers\n(SMC communities vs the rest)",
    titleLabSize = 14,
    pCutoff = 0.05/(nrow(SMC.markers)), # 20552 genes
    FCcutoff = 1.25,
    pointSize = 1.5,
    labSize = 3.0,
    legendLabels =c('NS','avg. fold-change','P',
      'P & avg. fold-change'),
    legendPosition = "right",
    legendLabSize = 10,
    legendIconSize = 3.0,
    drawConnectors = TRUE,
    widthConnectors = 0.2,
    colConnectors = "#595A5C",
    gridlines.major = FALSE,
    gridlines.minor = FALSE)
SMC_Volcano_TargetsA

ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.SMC.DEG.Targets.pdf"), 
       plot = SMC_Volcano_TargetsA)
Saving 18 x 12 in image

The target results are given below and written to a file.

library(tibble)
SMC.markers <- add_column(SMC.markers, Gene = row.names(SMC.markers), .before = 1)

temp <- SMC.markers[SMC.markers$Gene %in% target_genes_qc,]

DT::datatable(temp)
fwrite(temp, file = paste0(OUT_loc, "/", Today, ".SMC.DEG.Targets.txt"),
       quote = FALSE,
       sep = "\t", 
       showProgress = FALSE, verbose = FALSE)

Endothelial cells

Comparison between the endothelial cell communities (CD34+), and all other communities.


EC.markers <- FindMarkers(object = scRNAseqData, 
                          ident.1 = c("CD34+ EC I", 
                                      "CD34+ EC II"), 
                          ident.2 = c("CD68+CASP1+IL1B+SELL MInf", 
                                      "CD68+CD1C+ DC", 
                                      "CD68+CD4+ Mono",
                                      "CD68+IL18+TLR4+TREM2+ MRes",
                                      "CD68+ABCA1+OLR1+TREM2+ FC",
                                      "CD3+ TC I",
                                      "CD3+ TC II", 
                                      "CD3+ TC III", 
                                      "CD3+ TC IV", 
                                      "CD3+ TC V", 
                                      "CD3+ TC VI", 
                                      "FOXP3+ TC", 
                                      # "CD34+ EC I", 
                                      # "CD34+ EC II",
                                      "ACTA2+ SMC", 
                                      "CD3+CD56+ NK I",
                                      "CD3+CD56+ NK II", 
                                      "CD68+KIT+ MC",
                                      "CD79+ BCplasma", 
                                      "CD79A+ BCmem"))

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~01m 47s      
  |++                                                | 2 % ~01m 44s      
  |++                                                | 3 % ~01m 44s      
  |+++                                               | 4 % ~01m 45s      
  |+++                                               | 5 % ~01m 44s      
  |++++                                              | 6 % ~01m 42s      
  |++++                                              | 7 % ~01m 41s      
  |+++++                                             | 8 % ~01m 40s      
  |+++++                                             | 9 % ~01m 53s      
  |++++++                                            | 10% ~01m 50s      
  |++++++                                            | 11% ~01m 47s      
  |+++++++                                           | 12% ~01m 45s      
  |+++++++                                           | 13% ~01m 44s      
  |++++++++                                          | 14% ~01m 41s      
  |++++++++                                          | 15% ~01m 40s      
  |+++++++++                                         | 16% ~01m 38s      
  |+++++++++                                         | 17% ~01m 36s      
  |++++++++++                                        | 18% ~01m 35s      
  |++++++++++                                        | 19% ~01m 33s      
  |+++++++++++                                       | 20% ~01m 32s      
  |+++++++++++                                       | 21% ~01m 31s      
  |++++++++++++                                      | 22% ~07h 01m 16s  
  |++++++++++++                                      | 23% ~06h 37m 50s  
  |+++++++++++++                                     | 24% ~06h 16m 18s  
  |+++++++++++++                                     | 25% ~05h 56m 29s  
  |++++++++++++++                                    | 26% ~05h 38m 16s  
  |++++++++++++++                                    | 27% ~05h 21m 23s  
  |+++++++++++++++                                   | 28% ~05h 06m 56s  
  |+++++++++++++++                                   | 29% ~04h 52m 57s  
  |++++++++++++++++                                  | 30% ~04h 39m 22s  
  |++++++++++++++++                                  | 31% ~04h 26m 35s  
  |+++++++++++++++++                                 | 32% ~08h 25m 03s  
  |+++++++++++++++++                                 | 33% ~08h 02m 30s  
  |++++++++++++++++++                                | 34% ~07h 41m 17s  
  |++++++++++++++++++                                | 35% ~07h 21m 16s  
  |+++++++++++++++++++                               | 36% ~07h 02m 45s  
  |+++++++++++++++++++                               | 37% ~06h 45m 05s  
  |++++++++++++++++++++                              | 38% ~06h 28m 46s  
  |++++++++++++++++++++                              | 39% ~06h 12m 48s  
  |+++++++++++++++++++++                             | 40% ~05h 57m 30s  
  |+++++++++++++++++++++                             | 41% ~07h 17m 25s  
  |++++++++++++++++++++++                            | 42% ~06h 59m 41s  
  |++++++++++++++++++++++                            | 43% ~07h 01m 38s  
  |+++++++++++++++++++++++                           | 44% ~06h 44m 46s  
  |+++++++++++++++++++++++                           | 45% ~06h 28m 41s  
  |++++++++++++++++++++++++                          | 46% ~06h 13m 15s  
  |++++++++++++++++++++++++                          | 47% ~05h 58m 28s  
  |+++++++++++++++++++++++++                         | 48% ~05h 44m 19s  
  |+++++++++++++++++++++++++                         | 49% ~05h 30m 43s  
  |++++++++++++++++++++++++++                        | 51% ~05h 17m 39s  
  |++++++++++++++++++++++++++                        | 52% ~05h 05m 06s  
  |+++++++++++++++++++++++++++                       | 53% ~04h 53m 01s  
  |+++++++++++++++++++++++++++                       | 54% ~04h 41m 24s  
  |++++++++++++++++++++++++++++                      | 55% ~04h 30m 12s  
  |++++++++++++++++++++++++++++                      | 56% ~04h 19m 25s  
  |+++++++++++++++++++++++++++++                     | 57% ~04h 09m 01s  
  |+++++++++++++++++++++++++++++                     | 58% ~03h 58m 59s  
  |++++++++++++++++++++++++++++++                    | 59% ~03h 49m 17s  
  |++++++++++++++++++++++++++++++                    | 60% ~03h 39m 56s  
  |+++++++++++++++++++++++++++++++                   | 61% ~03h 30m 52s  
  |+++++++++++++++++++++++++++++++                   | 62% ~03h 22m 07s  
  |++++++++++++++++++++++++++++++++                  | 63% ~03h 13m 38s  
  |++++++++++++++++++++++++++++++++                  | 64% ~03h 05m 26s  
  |+++++++++++++++++++++++++++++++++                 | 65% ~03h 57m 51s  
  |+++++++++++++++++++++++++++++++++                 | 66% ~03h 47m 36s  
  |++++++++++++++++++++++++++++++++++                | 67% ~03h 37m 38s  
  |++++++++++++++++++++++++++++++++++                | 68% ~03h 27m 55s  
  |+++++++++++++++++++++++++++++++++++               | 69% ~03h 18m 29s  
  |+++++++++++++++++++++++++++++++++++               | 70% ~03h 09m 18s  
  |++++++++++++++++++++++++++++++++++++              | 71% ~03h 00m 24s  
  |++++++++++++++++++++++++++++++++++++              | 72% ~02h 51m 44s  
  |+++++++++++++++++++++++++++++++++++++             | 73% ~02h 43m 19s  
  |+++++++++++++++++++++++++++++++++++++             | 74% ~02h 35m 08s  
  |++++++++++++++++++++++++++++++++++++++            | 75% ~02h 27m 10s  
  |++++++++++++++++++++++++++++++++++++++            | 76% ~02h 19m 25s  
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~02h 11m 53s  
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~02h 04m 31s  
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~01h 57m 21s  
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~01h 50m 21s  
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~01h 43m 32s  
  |+++++++++++++++++++++++++++++++++++++++++         | 82% ~01h 36m 53s  
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~01h 30m 23s  
  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~01h 24m 03s  
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~01h 17m 52s  
  |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~01h 11m 49s  
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~01h 05m 55s  
  |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~01h 00m 09s  
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~54m 31s      
  |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~49m 00s      
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~43m 37s      
  |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~38m 21s      
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~33m 11s      
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~28m 09s      
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~23m 12s      
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~18m 22s      
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~13m 38s      
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~08m 60s      
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~04m 27s      
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=07h 16m 22s
DT::datatable(EC.markers)
EC_Volcano_TargetsA = EnhancedVolcano(EC.markers,
    lab = rownames(EC.markers),
    x = "avg_log2FC",
    y = "p_val_adj",
    selectLab = target_genes_qc,
    axisLabSize = 12,
    xlab = "average fold-change",
    title = "Endothelial cell markers\n(EC communities vs the rest)",
    titleLabSize = 14,
    pCutoff = 0.05/(nrow(EC.markers)), # 20552 genes
    FCcutoff = 1.25,
    pointSize = 1.5,
    labSize = 3.0,
    legendLabels =c('NS','avg. fold-change','P',
      'P & avg. fold-change'),
    legendPosition = "right",
    legendLabSize = 10,
    legendIconSize = 3.0,
    drawConnectors = TRUE,
    widthConnectors = 0.2,
    colConnectors = "#595A5C",
    gridlines.major = FALSE,
    gridlines.minor = FALSE)
EC_Volcano_TargetsA

ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.EC.DEG.Targets.pdf"), 
       plot = EC_Volcano_TargetsA)
Saving 18 x 12 in image

The target results are given below and written to a file.

library(tibble)
EC.markers <- add_column(EC.markers, Gene = row.names(EC.markers), .before = 1)

temp <- EC.markers[EC.markers$Gene %in% target_genes_qc,]

DT::datatable(temp)
fwrite(temp, file = paste0(OUT_loc, "/", Today, ".EC.DEG.Targets.txt"),
       quote = FALSE,
       sep = "\t", 
       showProgress = FALSE, verbose = FALSE)

T-cells

Comparison between the T-cell communities (CD3/CD4/CD8+), and all other communities.


TC.markers <- FindMarkers(object = scRNAseqData, 
                          ident.1 = c("CD3+ TC I",
                                      "CD3+ TC II", 
                                      "CD3+ TC III", 
                                      "CD3+ TC IV", 
                                      "CD3+ TC V", 
                                      "CD3+ TC VI", 
                                      "FOXP3+ TC"), 
                          ident.2 = c("CD68+CASP1+IL1B+SELL MInf", 
                                      "CD68+CD1C+ DC", 
                                      "CD68+CD4+ Mono",
                                      "CD68+IL18+TLR4+TREM2+ MRes",
                                      "CD68+ABCA1+OLR1+TREM2+ FC",
                                      # "CD3+ TC I",
                                      # "CD3+ TC II", 
                                      # "CD3+ TC III", 
                                      # "CD3+ TC IV", 
                                      # "CD3+ TC V", 
                                      # "CD3+ TC VI", 
                                      # "FOXP3+ TC", 
                                      "CD34+ EC I", 
                                      "CD34+ EC II",
                                      "ACTA2+ SMC", 
                                      "CD3+CD56+ NK I",
                                      "CD3+CD56+ NK II", 
                                      "CD68+KIT+ MC",
                                      "CD79+ BCplasma", 
                                      "CD79A+ BCmem"))

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~02m 09s      
  |++                                                | 2 % ~01m 56s      
  |++                                                | 3 % ~01m 57s      
  |+++                                               | 4 % ~01m 59s      
  |+++                                               | 5 % ~02m 02s      
  |++++                                              | 6 % ~02m 01s      
  |++++                                              | 7 % ~01m 59s      
  |+++++                                             | 8 % ~01m 59s      
  |+++++                                             | 9 % ~01m 58s      
  |++++++                                            | 10% ~01m 58s      
  |++++++                                            | 11% ~01m 56s      
  |+++++++                                           | 12% ~01m 55s      
  |+++++++                                           | 13% ~01m 54s      
  |++++++++                                          | 14% ~01m 51s      
  |++++++++                                          | 15% ~01m 49s      
  |+++++++++                                         | 16% ~01m 46s      
  |+++++++++                                         | 17% ~01m 45s      
  |++++++++++                                        | 18% ~01m 43s      
  |++++++++++                                        | 19% ~01m 42s      
  |+++++++++++                                       | 20% ~01m 41s      
  |+++++++++++                                       | 21% ~01m 39s      
  |++++++++++++                                      | 22% ~01m 37s      
  |++++++++++++                                      | 23% ~01m 35s      
  |+++++++++++++                                     | 24% ~01m 34s      
  |+++++++++++++                                     | 26% ~01m 33s      
  |++++++++++++++                                    | 27% ~01m 33s      
  |++++++++++++++                                    | 28% ~01m 31s      
  |+++++++++++++++                                   | 29% ~01m 29s      
  |+++++++++++++++                                   | 30% ~01m 28s      
  |++++++++++++++++                                  | 31% ~01m 26s      
  |++++++++++++++++                                  | 32% ~01m 24s      
  |+++++++++++++++++                                 | 33% ~01m 23s      
  |+++++++++++++++++                                 | 34% ~01m 21s      
  |++++++++++++++++++                                | 35% ~01m 20s      
  |++++++++++++++++++                                | 36% ~01m 19s      
  |+++++++++++++++++++                               | 37% ~01m 17s      
  |+++++++++++++++++++                               | 38% ~01m 16s      
  |++++++++++++++++++++                              | 39% ~01m 14s      
  |++++++++++++++++++++                              | 40% ~01m 13s      
  |+++++++++++++++++++++                             | 41% ~01m 12s      
  |+++++++++++++++++++++                             | 42% ~01m 11s      
  |++++++++++++++++++++++                            | 43% ~01m 10s      
  |++++++++++++++++++++++                            | 44% ~01m 09s      
  |+++++++++++++++++++++++                           | 45% ~01m 07s      
  |+++++++++++++++++++++++                           | 46% ~01m 06s      
  |++++++++++++++++++++++++                          | 47% ~01m 04s      
  |++++++++++++++++++++++++                          | 48% ~01m 06s      
  |+++++++++++++++++++++++++                         | 49% ~01m 04s      
  |+++++++++++++++++++++++++                         | 50% ~01m 03s      
  |++++++++++++++++++++++++++                        | 51% ~01m 01s      
  |+++++++++++++++++++++++++++                       | 52% ~60s          
  |+++++++++++++++++++++++++++                       | 53% ~58s          
  |++++++++++++++++++++++++++++                      | 54% ~57s          
  |++++++++++++++++++++++++++++                      | 55% ~56s          
  |+++++++++++++++++++++++++++++                     | 56% ~54s          
  |+++++++++++++++++++++++++++++                     | 57% ~53s          
  |++++++++++++++++++++++++++++++                    | 58% ~52s          
  |++++++++++++++++++++++++++++++                    | 59% ~50s          
  |+++++++++++++++++++++++++++++++                   | 60% ~49s          
  |+++++++++++++++++++++++++++++++                   | 61% ~48s          
  |++++++++++++++++++++++++++++++++                  | 62% ~47s          
  |++++++++++++++++++++++++++++++++                  | 63% ~45s          
  |+++++++++++++++++++++++++++++++++                 | 64% ~44s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~43s          
  |++++++++++++++++++++++++++++++++++                | 66% ~41s          
  |++++++++++++++++++++++++++++++++++                | 67% ~40s          
  |+++++++++++++++++++++++++++++++++++               | 68% ~38s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~37s          
  |++++++++++++++++++++++++++++++++++++              | 70% ~36s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~35s          
  |+++++++++++++++++++++++++++++++++++++             | 72% ~33s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~32s          
  |++++++++++++++++++++++++++++++++++++++            | 74% ~31s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~29s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~28s          
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~27s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~26s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~24s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~23s          
  |+++++++++++++++++++++++++++++++++++++++++         | 82% ~22s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~21s          
  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~20s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~18s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~17s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~16s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~15s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~13s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~12s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~11s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~10s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~08s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~07s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~06s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~05s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~04s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 59s
DT::datatable(TC.markers)
TC_Volcano_TargetsA = EnhancedVolcano(TC.markers,
    lab = rownames(TC.markers),
    x = "avg_log2FC",
    y = "p_val_adj",
    selectLab = target_genes_qc,
    axisLabSize = 12,
    xlab = "average fold-change",
    title = "T-cell markers\n(T-cell communities vs the rest)",
    titleLabSize = 14,
    pCutoff = 0.05/nrow(TC.markers), # 20552 genes
    FCcutoff = 1.25,
    pointSize = 1.5,
    labSize = 3.0,
    legendLabels =c('NS','avg. fold-change','P',
      'P & avg. fold-change'),
    legendPosition = "right",
    legendLabSize = 10,
    legendIconSize = 3.0,
    drawConnectors = TRUE,
    widthConnectors = 0.2,
    colConnectors = "#595A5C",
    gridlines.major = FALSE,
    gridlines.minor = FALSE)
TC_Volcano_TargetsA

ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.TC.DEG.Targets.pdf"), 
       plot = TC_Volcano_TargetsA)
Saving 18 x 12 in image

The target results are given below and written to a file.

library(tibble)
TC.markers <- add_column(TC.markers, Gene = row.names(TC.markers), .before = 1)

temp <- TC.markers[TC.markers$Gene %in% target_genes_qc,]

DT::datatable(temp)
fwrite(temp, file = paste0(OUT_loc, "/", Today, ".TC.DEG.Targets.txt"),
       quote = FALSE,
       sep = "\t", 
       showProgress = FALSE, verbose = FALSE)

B-cells

Comparison between the B-cell communities (CD79A+), and all other communities.


BC.markers <- FindMarkers(object = scRNAseqData, 
                          ident.1 = c("CD79+ BCplasma", 
                                      "CD79A+ BCmem"), 
                          ident.2 = c("CD68+CASP1+IL1B+SELL MInf", 
                                      "CD68+CD1C+ DC", 
                                      "CD68+CD4+ Mono",
                                      "CD68+IL18+TLR4+TREM2+ MRes",
                                      "CD68+ABCA1+OLR1+TREM2+ FC",
                                      "CD3+ TC I",
                                      "CD3+ TC II", 
                                      "CD3+ TC III", 
                                      "CD3+ TC IV", 
                                      "CD3+ TC V", 
                                      "CD3+ TC VI", 
                                      "FOXP3+ TC", 
                                      "CD34+ EC I", 
                                      "CD34+ EC II",
                                      "ACTA2+ SMC", 
                                      "CD3+CD56+ NK I",
                                      "CD3+CD56+ NK II", 
                                      "CD68+KIT+ MC"
                                      # "CD79+ BCplasma", 
                                      # "CD79A+ BCmem"
                                      ))

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~01m 48s      
  |+                                                 | 2 % ~01m 39s      
  |++                                                | 3 % ~01m 43s      
  |++                                                | 4 % ~01m 44s      
  |+++                                               | 5 % ~01m 40s      
  |+++                                               | 6 % ~01m 44s      
  |++++                                              | 7 % ~01m 44s      
  |++++                                              | 8 % ~01m 41s      
  |+++++                                             | 9 % ~01m 40s      
  |+++++                                             | 10% ~01m 38s      
  |++++++                                            | 11% ~01m 36s      
  |++++++                                            | 12% ~01m 50s      
  |+++++++                                           | 13% ~01m 48s      
  |+++++++                                           | 14% ~01m 45s      
  |++++++++                                          | 15% ~01m 42s      
  |++++++++                                          | 16% ~01m 40s      
  |+++++++++                                         | 17% ~01m 38s      
  |+++++++++                                         | 18% ~01m 36s      
  |++++++++++                                        | 19% ~01m 34s      
  |++++++++++                                        | 20% ~01m 32s      
  |+++++++++++                                       | 21% ~01m 30s      
  |+++++++++++                                       | 22% ~01m 29s      
  |++++++++++++                                      | 23% ~01m 27s      
  |++++++++++++                                      | 24% ~01m 26s      
  |+++++++++++++                                     | 25% ~01m 25s      
  |+++++++++++++                                     | 26% ~01m 24s      
  |++++++++++++++                                    | 27% ~01m 22s      
  |++++++++++++++                                    | 28% ~01m 21s      
  |+++++++++++++++                                   | 29% ~01m 19s      
  |+++++++++++++++                                   | 30% ~01m 18s      
  |++++++++++++++++                                  | 31% ~01m 16s      
  |++++++++++++++++                                  | 32% ~01m 15s      
  |+++++++++++++++++                                 | 33% ~01m 14s      
  |+++++++++++++++++                                 | 34% ~01m 12s      
  |++++++++++++++++++                                | 35% ~01m 11s      
  |++++++++++++++++++                                | 36% ~01m 10s      
  |+++++++++++++++++++                               | 37% ~01m 09s      
  |+++++++++++++++++++                               | 38% ~01m 08s      
  |++++++++++++++++++++                              | 39% ~01m 07s      
  |++++++++++++++++++++                              | 40% ~01m 05s      
  |+++++++++++++++++++++                             | 41% ~01m 05s      
  |+++++++++++++++++++++                             | 42% ~01m 03s      
  |++++++++++++++++++++++                            | 43% ~01m 02s      
  |++++++++++++++++++++++                            | 44% ~01m 01s      
  |+++++++++++++++++++++++                           | 45% ~60s          
  |+++++++++++++++++++++++                           | 46% ~59s          
  |++++++++++++++++++++++++                          | 47% ~58s          
  |++++++++++++++++++++++++                          | 48% ~56s          
  |+++++++++++++++++++++++++                         | 49% ~55s          
  |+++++++++++++++++++++++++                         | 50% ~54s          
  |++++++++++++++++++++++++++                        | 51% ~53s          
  |++++++++++++++++++++++++++                        | 52% ~52s          
  |+++++++++++++++++++++++++++                       | 53% ~51s          
  |+++++++++++++++++++++++++++                       | 54% ~50s          
  |++++++++++++++++++++++++++++                      | 55% ~49s          
  |++++++++++++++++++++++++++++                      | 56% ~48s          
  |+++++++++++++++++++++++++++++                     | 57% ~46s          
  |+++++++++++++++++++++++++++++                     | 58% ~45s          
  |++++++++++++++++++++++++++++++                    | 59% ~44s          
  |++++++++++++++++++++++++++++++                    | 60% ~43s          
  |+++++++++++++++++++++++++++++++                   | 61% ~42s          
  |+++++++++++++++++++++++++++++++                   | 62% ~41s          
  |++++++++++++++++++++++++++++++++                  | 63% ~40s          
  |++++++++++++++++++++++++++++++++                  | 64% ~39s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~38s          
  |+++++++++++++++++++++++++++++++++                 | 66% ~37s          
  |++++++++++++++++++++++++++++++++++                | 67% ~35s          
  |++++++++++++++++++++++++++++++++++                | 68% ~34s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~33s          
  |+++++++++++++++++++++++++++++++++++               | 70% ~32s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~31s          
  |++++++++++++++++++++++++++++++++++++              | 72% ~30s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~29s          
  |+++++++++++++++++++++++++++++++++++++             | 74% ~28s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~27s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~26s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~24s          
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~23s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~22s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~21s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~20s          
  |+++++++++++++++++++++++++++++++++++++++++         | 82% ~19s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~18s          
  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~17s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~16s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~15s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~14s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~13s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~12s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~11s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~10s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~08s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~07s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~06s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~05s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~04s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 46s
DT::datatable(BC.markers)
BC_Volcano_TargetsA = EnhancedVolcano(BC.markers,
    lab = rownames(BC.markers),
    x = "avg_log2FC",
    y = "p_val_adj",
    selectLab = target_genes_qc,
    axisLabSize = 12,
    xlab = "average fold-change",
    title = "B-cell markers\n(B-cell communities vs the rest)",
    titleLabSize = 14,
    pCutoff = 0.05/nrow(BC.markers), # 20552 genes
    FCcutoff = 1.25,
    pointSize = 1.5,
    labSize = 3.0,
    legendLabels =c('NS','avg. fold-change','P',
      'P & avg. fold-change'),
    legendPosition = "right",
    legendLabSize = 10,
    legendIconSize = 3.0,
    drawConnectors = TRUE,
    widthConnectors = 0.2,
    colConnectors = "#595A5C",
    gridlines.major = FALSE,
    gridlines.minor = FALSE)
BC_Volcano_TargetsA

ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.BC.DEG.Targets.pdf"), 
       plot = BC_Volcano_TargetsA)
Saving 18 x 12 in image

The target results are given below and written to a file.

library(tibble)
BC.markers <- add_column(BC.markers, Gene = row.names(BC.markers), .before = 1)

temp <- BC.markers[BC.markers$Gene %in% target_genes_qc,]

DT::datatable(temp)
fwrite(temp, file = paste0(OUT_loc, "/", Today, ".BC.DEG.Targets.txt"),
       quote = FALSE,
       sep = "\t", 
       showProgress = FALSE, verbose = FALSE)

Mast cells

Comparison between the mast cell communities (KIT+), and all other communities.


MC.markers <- FindMarkers(object = scRNAseqData, 
                          ident.1 = c("CD68+KIT+ MC"), 
                          ident.2 = c("CD68+CASP1+IL1B+SELL MInf", 
                                      "CD68+CD1C+ DC", 
                                      "CD68+CD4+ Mono",
                                      "CD68+IL18+TLR4+TREM2+ MRes",
                                      "CD68+ABCA1+OLR1+TREM2+ FC",
                                      "CD3+ TC I",
                                      "CD3+ TC II", 
                                      "CD3+ TC III", 
                                      "CD3+ TC IV", 
                                      "CD3+ TC V", 
                                      "CD3+ TC VI", 
                                      "FOXP3+ TC", 
                                      "CD34+ EC I", 
                                      "CD34+ EC II",
                                      "ACTA2+ SMC", 
                                      "CD3+CD56+ NK I",
                                      "CD3+CD56+ NK II", 
                                      # "CD68+KIT+ MC",
                                      "CD79+ BCplasma", 
                                      "CD79A+ BCmem"))

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~02m 08s      
  |+                                                 | 2 % ~02m 20s      
  |++                                                | 3 % ~02m 22s      
  |++                                                | 4 % ~02m 23s      
  |+++                                               | 5 % ~02m 24s      
  |+++                                               | 6 % ~02m 24s      
  |++++                                              | 7 % ~02m 20s      
  |++++                                              | 8 % ~02m 19s      
  |+++++                                             | 9 % ~02m 17s      
  |+++++                                             | 10% ~02m 18s      
  |++++++                                            | 11% ~02m 16s      
  |++++++                                            | 12% ~02m 15s      
  |+++++++                                           | 13% ~02m 13s      
  |+++++++                                           | 14% ~02m 12s      
  |++++++++                                          | 15% ~02m 11s      
  |++++++++                                          | 16% ~02m 10s      
  |+++++++++                                         | 17% ~02m 09s      
  |+++++++++                                         | 18% ~02m 07s      
  |++++++++++                                        | 19% ~02m 05s      
  |++++++++++                                        | 20% ~02m 03s      
  |+++++++++++                                       | 21% ~02m 01s      
  |+++++++++++                                       | 22% ~01m 59s      
  |++++++++++++                                      | 23% ~01m 57s      
  |++++++++++++                                      | 24% ~01m 56s      
  |+++++++++++++                                     | 25% ~01m 54s      
  |+++++++++++++                                     | 26% ~01m 53s      
  |++++++++++++++                                    | 27% ~01m 51s      
  |++++++++++++++                                    | 28% ~01m 49s      
  |+++++++++++++++                                   | 29% ~01m 47s      
  |+++++++++++++++                                   | 30% ~01m 45s      
  |++++++++++++++++                                  | 31% ~01m 43s      
  |++++++++++++++++                                  | 32% ~01m 42s      
  |+++++++++++++++++                                 | 33% ~01m 40s      
  |+++++++++++++++++                                 | 34% ~01m 38s      
  |++++++++++++++++++                                | 35% ~01m 36s      
  |++++++++++++++++++                                | 36% ~01m 34s      
  |+++++++++++++++++++                               | 37% ~01m 33s      
  |+++++++++++++++++++                               | 38% ~01m 31s      
  |++++++++++++++++++++                              | 39% ~01m 29s      
  |++++++++++++++++++++                              | 40% ~01m 28s      
  |+++++++++++++++++++++                             | 41% ~01m 26s      
  |+++++++++++++++++++++                             | 42% ~01m 25s      
  |++++++++++++++++++++++                            | 43% ~01m 23s      
  |++++++++++++++++++++++                            | 44% ~01m 21s      
  |+++++++++++++++++++++++                           | 45% ~01m 20s      
  |+++++++++++++++++++++++                           | 46% ~01m 18s      
  |++++++++++++++++++++++++                          | 47% ~01m 17s      
  |++++++++++++++++++++++++                          | 48% ~01m 15s      
  |+++++++++++++++++++++++++                         | 49% ~01m 14s      
  |+++++++++++++++++++++++++                         | 50% ~01m 12s      
  |++++++++++++++++++++++++++                        | 51% ~01m 10s      
  |++++++++++++++++++++++++++                        | 52% ~01m 09s      
  |+++++++++++++++++++++++++++                       | 53% ~01m 07s      
  |+++++++++++++++++++++++++++                       | 54% ~01m 06s      
  |++++++++++++++++++++++++++++                      | 55% ~01m 04s      
  |++++++++++++++++++++++++++++                      | 56% ~01m 03s      
  |+++++++++++++++++++++++++++++                     | 57% ~01m 01s      
  |+++++++++++++++++++++++++++++                     | 58% ~60s          
  |++++++++++++++++++++++++++++++                    | 59% ~58s          
  |++++++++++++++++++++++++++++++                    | 60% ~57s          
  |+++++++++++++++++++++++++++++++                   | 61% ~55s          
  |+++++++++++++++++++++++++++++++                   | 62% ~54s          
  |++++++++++++++++++++++++++++++++                  | 63% ~52s          
  |++++++++++++++++++++++++++++++++                  | 64% ~51s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~49s          
  |+++++++++++++++++++++++++++++++++                 | 66% ~48s          
  |++++++++++++++++++++++++++++++++++                | 67% ~48s          
  |++++++++++++++++++++++++++++++++++                | 68% ~46s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~45s          
  |+++++++++++++++++++++++++++++++++++               | 70% ~43s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~42s          
  |++++++++++++++++++++++++++++++++++++              | 72% ~40s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~39s          
  |+++++++++++++++++++++++++++++++++++++             | 74% ~37s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~36s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~34s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~33s          
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~31s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~30s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~28s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~27s          
  |+++++++++++++++++++++++++++++++++++++++++         | 82% ~25s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~24s          
  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~23s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~21s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~20s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~18s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~17s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~15s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~14s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~13s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~11s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~10s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~08s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~07s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~06s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~04s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~03s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02m 17s
DT::datatable(MC.markers)
MC_Volcano_TargetsA = EnhancedVolcano(MC.markers,
    lab = rownames(MC.markers),
    x = "avg_log2FC",
    y = "p_val_adj",
    selectLab = target_genes_qc,
    axisLabSize = 12,
    xlab = "average fold-change",
    title = "Mast cell markers\n(Mast cell communities vs the rest)",
    titleLabSize = 14,
    pCutoff = 0.05/nrow(MC.markers), # 20552 genes
    FCcutoff = 1.25,
    pointSize = 1.5,
    labSize = 3.0,
    legendLabels =c('NS','avg. fold-change','P',
      'P & avg. fold-change'),
    legendPosition = "right",
    legendLabSize = 10,
    legendIconSize = 3.0,
    drawConnectors = TRUE,
    widthConnectors = 0.2,
    colConnectors = "#595A5C",
    gridlines.major = FALSE,
    gridlines.minor = FALSE)
MC_Volcano_TargetsA

ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.MC.DEG.Targets.pdf"), 
       plot = MC_Volcano_TargetsA)
Saving 18 x 12 in image

The target results are given below and written to a file.

library(tibble)
MC.markers <- add_column(MC.markers, Gene = row.names(MC.markers), .before = 1)

temp <- MC.markers[MC.markers$Gene %in% target_genes_qc,]

DT::datatable(temp)
fwrite(temp, file = paste0(OUT_loc, "/", Today, ".MC.DEG.Targets.txt"),
       quote = FALSE,
       sep = "\t", 
       showProgress = FALSE, verbose = FALSE)

NK-cells

Comparison between the natural killer cell communities (NCAM1+), and all other communities.


NK.markers <- FindMarkers(object = scRNAseqData, 
                          ident.1 = c("CD3+CD56+ NK I",
                                      "CD3+CD56+ NK II"), 
                          ident.2 = c("CD68+CASP1+IL1B+SELL MInf", 
                                      "CD68+CD1C+ DC", 
                                      "CD68+CD4+ Mono",
                                      "CD68+IL18+TLR4+TREM2+ MRes",
                                      "CD68+ABCA1+OLR1+TREM2+ FC",
                                      "CD3+ TC I",
                                      "CD3+ TC II", 
                                      "CD3+ TC III", 
                                      "CD3+ TC IV", 
                                      "CD3+ TC V", 
                                      "CD3+ TC VI", 
                                      "FOXP3+ TC", 
                                      "CD34+ EC I", 
                                      "CD34+ EC II",
                                      "ACTA2+ SMC", 
                                      # "CD3+CD56+ NK I",
                                      # "CD3+CD56+ NK II", 
                                      "CD68+KIT+ MC",
                                      "CD79+ BCplasma", 
                                      "CD79A+ BCmem"))

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~56s          
  |++                                                | 2 % ~56s          
  |++                                                | 3 % ~55s          
  |+++                                               | 4 % ~59s          
  |+++                                               | 5 % ~57s          
  |++++                                              | 6 % ~56s          
  |++++                                              | 7 % ~55s          
  |+++++                                             | 8 % ~54s          
  |+++++                                             | 9 % ~53s          
  |++++++                                            | 10% ~53s          
  |++++++                                            | 11% ~51s          
  |+++++++                                           | 12% ~51s          
  |+++++++                                           | 13% ~51s          
  |++++++++                                          | 14% ~50s          
  |++++++++                                          | 15% ~49s          
  |+++++++++                                         | 16% ~48s          
  |+++++++++                                         | 18% ~48s          
  |++++++++++                                        | 19% ~47s          
  |++++++++++                                        | 20% ~47s          
  |+++++++++++                                       | 21% ~46s          
  |+++++++++++                                       | 22% ~45s          
  |++++++++++++                                      | 23% ~45s          
  |++++++++++++                                      | 24% ~44s          
  |+++++++++++++                                     | 25% ~43s          
  |+++++++++++++                                     | 26% ~43s          
  |++++++++++++++                                    | 27% ~42s          
  |++++++++++++++                                    | 28% ~41s          
  |+++++++++++++++                                   | 29% ~41s          
  |+++++++++++++++                                   | 30% ~40s          
  |++++++++++++++++                                  | 31% ~40s          
  |++++++++++++++++                                  | 32% ~40s          
  |+++++++++++++++++                                 | 33% ~39s          
  |++++++++++++++++++                                | 34% ~38s          
  |++++++++++++++++++                                | 35% ~38s          
  |+++++++++++++++++++                               | 36% ~37s          
  |+++++++++++++++++++                               | 37% ~37s          
  |++++++++++++++++++++                              | 38% ~36s          
  |++++++++++++++++++++                              | 39% ~35s          
  |+++++++++++++++++++++                             | 40% ~35s          
  |+++++++++++++++++++++                             | 41% ~34s          
  |++++++++++++++++++++++                            | 42% ~33s          
  |++++++++++++++++++++++                            | 43% ~33s          
  |+++++++++++++++++++++++                           | 44% ~32s          
  |+++++++++++++++++++++++                           | 45% ~32s          
  |++++++++++++++++++++++++                          | 46% ~31s          
  |++++++++++++++++++++++++                          | 47% ~30s          
  |+++++++++++++++++++++++++                         | 48% ~30s          
  |+++++++++++++++++++++++++                         | 49% ~29s          
  |++++++++++++++++++++++++++                        | 51% ~28s          
  |++++++++++++++++++++++++++                        | 52% ~28s          
  |+++++++++++++++++++++++++++                       | 53% ~27s          
  |+++++++++++++++++++++++++++                       | 54% ~27s          
  |++++++++++++++++++++++++++++                      | 55% ~26s          
  |++++++++++++++++++++++++++++                      | 56% ~27s          
  |+++++++++++++++++++++++++++++                     | 57% ~26s          
  |+++++++++++++++++++++++++++++                     | 58% ~26s          
  |++++++++++++++++++++++++++++++                    | 59% ~25s          
  |++++++++++++++++++++++++++++++                    | 60% ~24s          
  |+++++++++++++++++++++++++++++++                   | 61% ~24s          
  |+++++++++++++++++++++++++++++++                   | 62% ~23s          
  |++++++++++++++++++++++++++++++++                  | 63% ~22s          
  |++++++++++++++++++++++++++++++++                  | 64% ~22s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~21s          
  |+++++++++++++++++++++++++++++++++                 | 66% ~20s          
  |++++++++++++++++++++++++++++++++++                | 67% ~20s          
  |+++++++++++++++++++++++++++++++++++               | 68% ~19s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~18s          
  |++++++++++++++++++++++++++++++++++++              | 70% ~18s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~17s          
  |+++++++++++++++++++++++++++++++++++++             | 72% ~17s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~16s          
  |++++++++++++++++++++++++++++++++++++++            | 74% ~15s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~15s          
  |+++++++++++++++++++++++++++++++++++++++           | 76% ~14s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~14s          
  |++++++++++++++++++++++++++++++++++++++++          | 78% ~13s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~13s          
  |+++++++++++++++++++++++++++++++++++++++++         | 80% ~12s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~11s          
  |++++++++++++++++++++++++++++++++++++++++++        | 82% ~11s          
  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~10s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~09s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~09s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~08s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~07s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~07s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~06s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~06s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~05s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~04s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~04s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~03s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=60s  
DT::datatable(NK.markers)
NK_Volcano_TargetsA = EnhancedVolcano(NK.markers,
    lab = rownames(NK.markers),
    x = "avg_log2FC",
    y = "p_val_adj",
    selectLab = target_genes_qc,
    axisLabSize = 12,
    xlab = "average fold-change",
    title = "NK markers\n(NK-cell communities vs the rest)",
    titleLabSize = 14,
    pCutoff = 0.05/nrow(NK.markers), # 20552 genes
    FCcutoff = 1.25,
    pointSize = 1.5,
    labSize = 3.0,
    legendLabels =c('NS','avg. fold-change','P',
      'P & avg. fold-change'),
    legendPosition = "right",
    legendLabSize = 10,
    legendIconSize = 3.0,
    drawConnectors = TRUE,
    widthConnectors = 0.2,
    colConnectors = "#595A5C",
    gridlines.major = FALSE,
    gridlines.minor = FALSE)
NK_Volcano_TargetsA

ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.NK.DEG.Targets.pdf"), 
       plot = NK_Volcano_TargetsA)
Saving 18 x 12 in image

The target results are given below and written to a file.

library(tibble)
NK.markers <- add_column(NK.markers, Gene = row.names(NK.markers), .before = 1)

temp <- NK.markers[NK.markers$Gene %in% target_genes_qc,]

DT::datatable(temp)
fwrite(temp, file = paste0(OUT_loc, "/", Today, ".NK.DEG.Targets.txt"),
       quote = FALSE,
       sep = "\t", 
       showProgress = FALSE, verbose = FALSE)

Subset scRNAseq data

List of samples to be included based on informed consent (see above).

samples_of_interest <- unlist(scRNAseqDataMetaAE.all$Patient)
scRNAseqDataCEA39 <- subset(scRNAseqData, subset = Patient %in% samples_of_interest)
variables_of_interest <- c("Hospital", "ORyear", "Artery_summary",
                           "Age", "Gender",
                           "TC_final", "LDL_final", "HDL_final", "TG_final",
                           "systolic", "diastoli", "GFR_MDRD", "BMI",
                           "KDOQI", "BMI_WHO",
                           "SmokerStatus", "AlcoholUse",
                           "DiabetesStatus",
                           "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs",
                           "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD",
                           "Stroke_Dx",
                           "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
                           "Symptoms.Update2G", "Symptoms.Update3G", "indexsymptoms_latest_4g",
                           "restenos", "stenose",
                           "CAD_history", "PAOD", "Peripheral.interv",
                           "EP_composite", "EP_composite_time", "EP_major", "EP_major_time")

temp <- subset(scRNAseqDataMetaAE.all, select = c("Patient", variables_of_interest))
# str(temp)
scRNAseqDataCEA39@meta.data <- merge(scRNAseqDataCEA39@meta.data, temp, by.x = "Patient", by.y = "Patient")
scRNAseqDataCEA39@meta.data <- dplyr::rename(scRNAseqDataCEA39@meta.data, "STUDY_NUMBER" = "Patient")

# str(scRNAseqDataCEA39@meta.data)

Saving new dataset

temp2 <- as_tibble(subset(scRNAseqDataCEA39@meta.data, select = c("STUDY_NUMBER", "orig.ident", "nCount_RNA", "nFeature_RNA",
                                                                 "Plate", "Batch", "C.H", "Type", "percent.mt",
                                                                 "nCount_SCT", "nFeature_SCT", "seurat_clusters")))

# fwrite(temp2,
#        file = paste0(OUT_loc, "/", Today, ".AESCRNA.CEA.39pts.samplelist.after_qc.IC_commercial.csv"),
#        sep = ",", row.names = FALSE, col.names = TRUE,
#        showProgress = TRUE)
# rm(temp2)
# 
# temp <- dplyr::rename(temp, "STUDY_NUMBER" = "Patient")
# fwrite(temp,
#        file = paste0(OUT_loc, "/", Today, ".AESCRNA.CEA.39pts.clinicaldata.after_qc.IC_commercial.csv"),
#        sep = ",", row.names = FALSE, col.names = TRUE,
#        showProgress = TRUE)
# rm(temp)
# 
# saveRDS(scRNAseqDataCEA39, file = paste0(OUT_loc, "/", Today, ".AESCRNA.CEA.39pts.Seurat.after_qc.IC_commercial.RDS"))

fwrite(temp2,
       file = paste0(OUT_loc, "/", Today, ".AESCRNA.CEA.39pts.samplelist.after_qc.IC_academic.csv"),
       sep = ",", row.names = FALSE, col.names = TRUE,
       showProgress = TRUE)
rm(temp2)

temp <- dplyr::rename(temp, "STUDY_NUMBER" = "Patient")
fwrite(temp,
       file = paste0(OUT_loc, "/", Today, ".AESCRNA.CEA.39pts.clinicaldata.after_qc.IC_academic.csv"),
       sep = ",", row.names = FALSE, col.names = TRUE,
       showProgress = TRUE)
rm(temp)

saveRDS(scRNAseqDataCEA39, file = paste0(OUT_loc, "/", Today, ".AESCRNA.CEA.39pts.Seurat.after_qc.IC_academic.RDS"))

Session information


Version:      v1.0.0
Last update:  2022-03-17
Written by:   Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
Description:  Script to load single-cell RNA sequencing (scRNAseq) data, and perform quality control (QC), and initial mapping to cells.
Minimum requirements: R version 3.5.2 (2018-12-20) -- 'Eggshell Igloo', macOS Mojave (10.14.2).

Change log
* v1.0.0 Initial version.

sessionInfo()
R version 4.1.2 (2021-11-01)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Monterey 12.2.1

Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
 [1] stats4    grid      tools     stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] RColorBrewer_1.1-2     labelled_2.9.0         SeuratObject_4.0.4     Seurat_4.1.0           EnhancedVolcano_1.12.0 ggrepel_0.9.1         
 [7] mygene_1.30.0          GenomicFeatures_1.46.5 GenomicRanges_1.46.1   GenomeInfoDb_1.30.1    org.Hs.eg.db_3.14.0    AnnotationDbi_1.56.2  
[13] IRanges_2.28.0         S4Vectors_0.32.3       Biobase_2.54.0         BiocGenerics_0.40.0    UpSetR_1.4.0           ggpubr_0.4.0          
[19] forestplot_2.0.1       checkmate_2.0.0        magrittr_2.0.2         pheatmap_1.0.12        devtools_2.4.3         usethis_2.1.5         
[25] BlandAltmanLeh_0.3.1   sjPlot_2.8.10          tableone_0.13.0        haven_2.4.3            openxlsx_4.2.5         eeptools_1.2.4        
[31] DT_0.21                knitr_1.37             forcats_0.5.1          stringr_1.4.0          purrr_0.3.4            tibble_3.1.6          
[37] ggplot2_3.3.5          tidyverse_1.3.1        data.table_1.14.2      naniar_0.6.1           tidyr_1.2.0            dplyr_1.0.8           
[43] optparse_1.7.1         readr_2.1.2            pander_0.6.4           rmarkdown_2.13         worcs_0.1.9.1         

loaded via a namespace (and not attached):
  [1] vcd_1.4-9                   Hmisc_4.6-0                 ica_1.0-2                   class_7.3-20                ps_1.6.0                   
  [6] Rsamtools_2.10.0            lmtest_0.9-39               rprojroot_2.0.2             crayon_1.5.0                spatstat.core_2.4-0        
 [11] MASS_7.3-55                 nlme_3.1-155                backports_1.4.1             reprex_2.0.1                rlang_1.0.2                
 [16] XVector_0.34.0              ROCR_1.0-11                 readxl_1.3.1                performance_0.8.0           irlba_2.3.5                
 [21] extrafontdb_1.0             nloptr_2.0.0                callr_3.7.0                 filelock_1.0.2              proto_1.0.0                
 [26] extrafont_0.17              BiocParallel_1.28.3         rjson_0.2.21                bit64_4.0.5                 glue_1.6.2                 
 [31] sctransform_0.3.3           vipor_0.4.5                 parallel_4.1.2              processx_3.5.2              spatstat.sparse_2.1-0      
 [36] spatstat.geom_2.3-2         tidyselect_1.1.2            SummarizedExperiment_1.24.0 fitdistrplus_1.1-8          XML_3.99-0.9               
 [41] zoo_1.8-9                   proj4_1.0-11                sjmisc_2.8.9                GenomicAlignments_1.30.0    chron_2.3-56               
 [46] xtable_1.8-4                evaluate_0.15               cli_3.2.0                   zlibbioc_1.40.0             rstudioapi_0.13            
 [51] miniUI_0.1.1.1              sp_1.4-6                    bslib_0.3.1                 rpart_4.1.16                sjlabelled_1.1.8           
 [56] ensembldb_2.18.3            maps_3.4.0                  shiny_1.7.1                 xfun_0.30                   askpass_1.1                
 [61] parameters_0.17.0           pkgbuild_1.3.1              cluster_2.1.2               KEGGREST_1.34.0             listenv_0.8.0              
 [66] Biostrings_2.62.0           png_0.1-7                   future_1.24.0               withr_2.5.0                 bitops_1.0-7               
 [71] ranger_0.13.1               plyr_1.8.6                  cellranger_1.1.0            sys_3.4                     AnnotationFilter_1.18.0    
 [76] e1071_1.7-9                 survey_4.1-1                coda_0.19-4                 pillar_1.7.0                cachem_1.0.6               
 [81] fs_1.5.2                    vctrs_0.3.8                 ellipsis_0.3.2              generics_0.1.2              gsubfn_0.7                 
 [86] foreign_0.8-82              beeswarm_0.4.0              munsell_0.5.0               proxy_0.4-26                emmeans_1.7.2              
 [91] DelayedArray_0.20.0         fastmap_1.1.0               compiler_4.1.2              pkgload_1.2.4               abind_1.4-5                
 [96] httpuv_1.6.5                rtracklayer_1.54.0          sessioninfo_1.2.2           plotly_4.10.0               GenomeInfoDbData_1.2.7     
[101] gridExtra_2.3               gert_1.5.0                  lattice_0.20-45             deldir_1.0-6                utf8_1.2.2                 
[106] later_1.3.0                 BiocFileCache_2.2.1         jsonlite_1.8.0              arm_1.12-2                  credentials_1.3.2          
[111] scales_1.1.1                pbapply_1.5-0               carData_3.0-5               estimability_1.3            renv_0.15.4                
[116] lazyeval_0.2.2              promises_1.2.0.1            car_3.0-12                  latticeExtra_0.6-29         goftest_1.2-3              
[121] spatstat.utils_2.3-0        reticulate_1.24             effectsize_0.6.0.1          ash_1.0-15                  cowplot_1.1.1              
[126] textshaping_0.3.6           Rtsne_0.15                  uwot_0.1.11                 igraph_1.2.11               survival_3.3-1             
[131] yaml_2.3.5                  systemfonts_1.0.4           htmltools_0.5.2             memoise_2.0.1               BiocIO_1.4.0               
[136] viridisLite_0.4.0           digest_0.6.29               assertthat_0.2.1            mime_0.12                   rappdirs_0.3.3             
[141] Rttf2pt1_1.3.10             bayestestR_0.11.5           RSQLite_2.2.10              sqldf_0.4-11                future.apply_1.8.1         
[146] remotes_2.4.2               blob_1.2.2                  ragg_1.2.2                  labeling_0.4.2              Formula_1.2-4              
[151] splines_4.1.2               Cairo_1.5-15                ProtGenerics_1.26.0         RCurl_1.98-1.6              broom_0.7.12               
[156] hms_1.1.1                   modelr_0.1.8                base64enc_0.1-3             colorspace_2.0-3            BiocManager_1.30.16        
[161] ggbeeswarm_0.6.0            ggrastr_1.0.1               nnet_7.3-17                 sass_0.4.0                  Rcpp_1.0.8.3               
[166] RANN_2.6.1                  mvtnorm_1.1-3               fansi_1.0.2                 tzdb_0.2.0                  brio_1.1.3                 
[171] parallelly_1.30.0           R6_2.5.1                    ggridges_0.5.3              lifecycle_1.0.1             zip_2.2.0                  
[176] datawizard_0.3.0            curl_4.3.2                  ggsignif_0.6.3              minqa_1.2.4                 jquerylib_0.1.4            
[181] leiden_0.3.9                testthat_3.1.2              getopt_1.20.3               rticles_0.23                Matrix_1.4-0               
[186] prereg_0.6.0                desc_1.4.1                  RcppAnnoy_0.0.19            htmlwidgets_1.5.4           polyclip_1.10-0            
[191] biomaRt_2.50.3              crosstalk_1.2.0             rvest_1.0.2                 mgcv_1.8-39                 globals_0.14.0             
[196] openssl_2.0.0               insight_0.16.0              htmlTable_2.4.0             patchwork_1.1.0.9000        spatstat.random_2.1-0      
[201] codetools_0.2-18            matrixStats_0.61.0          lubridate_1.8.0             prettyunits_1.1.1           dbplyr_2.1.1               
[206] gtable_0.3.0                DBI_1.1.2                   visdat_0.5.3                tensor_1.5                  httr_1.4.2                 
[211] KernSmooth_2.23-20          stringi_1.7.6               progress_1.2.2              farver_2.1.0                reshape2_1.4.4             
[216] xml2_1.3.3                  boot_1.3-28                 ggeffects_1.1.1             ggalt_0.4.0                 lme4_1.1-28                
[221] restfulr_0.0.13             scattermore_0.8             bit_4.0.4                   jpeg_0.1-9                  sjstats_0.18.1             
[226] MatrixGenerics_1.6.0        spatstat.data_2.1-2         pkgconfig_2.0.3             maptools_1.1-3              rstatix_0.7.0              
[231] mitools_2.4                

Saving environment

rm(backup.scRNAseqData)
Warning in rm(backup.scRNAseqData) :
  object 'backup.scRNAseqData' not found
rm(scRNAseqData, scRNAseqDataCEA39)

save.image(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".AESCRNA.results.RData"))
© 1979-2022 Sander W. van der Laan | s.w.vanderlaan[at]gmail.com swvanderlaan.github.io.
---
title: "Plaque expression levels of _HDAC9_ in association with plaque vulnerability traits and secondary vascular events in patients undergoing carotid endarterectomy: an analysis in the Athero-EXPRESS Biobank."
author: "[Sander W. van der Laan, PhD](https://swvanderlaan.github.io) | @swvanderlaan | s.w.vanderlaan@gmail.com"
date: "`r Sys.Date()`"
output:
  html_notebook:
    cache: yes
    code_folding: hide
    collapse: yes
    df_print: paged
    fig.align: center
    fig_caption: yes
    fig_height: 6
    fig_retina: 2
    fig_width: 7
    highlight: tango
    theme: lumen
    toc: yes
    toc_float:
      collapsed: no
      smooth_scroll: yes
mainfont: Arial
subtitle: "Athero-Express single-cell RNA sequencing analysis"
editor_options:
  chunk_output_type: inline
# bibliography: references.bib
# knit: worcs::cite_all
---

# General Setup

```{r setup, include=FALSE}
# We recommend that you prepare your raw data for analysis in 'prepare_data.R',
# and end that file with either open_data(yourdata), or closed_data(yourdata).
# Then, uncomment the line below to load the original or synthetic data
# (whichever is available), to allow anyone to reproduce your code:
# load_data()

# further define some knitr-options.
knitr::opts_chunk$set(fig.width = 12, fig.height = 8, fig.path = 'Figures/', 
                      warning = TRUE, # show warnings during codebook generation
                      message = TRUE, # show messages during codebook generation
                      error = TRUE, # do not interrupt codebook generation in case of errors, 
                                    # usually better for debugging
                      echo = TRUE,  # show R code
                      eval = TRUE)

ggplot2::theme_set(ggplot2::theme_minimal())
# pander::panderOptions("table.split.table", Inf)
library("worcs")
library("rmarkdown")

```

```{r echo = FALSE}
rm(list = ls())
```

```{r LocalSystem, echo = FALSE}
### Operating System Version
### MacBook Pro
ROOT_loc = "/Users/swvanderlaan"

### MacBook Air 
# ROOT_loc = "/Users/slaan3"

### General
GENOMIC_loc = paste0(ROOT_loc, "/OneDrive - UMC Utrecht/Genomics")
AEDB_loc = paste0(GENOMIC_loc, "/Athero-Express/AE-AAA_GS_DBs")
LAB_loc = paste0(GENOMIC_loc, "/LabBusiness")

PROJECT_loc = paste0(ROOT_loc, "/git/CirculatoryHealth/AE_20211201_YAW_SWVANDERLAAN_HDAC9")

# Genetic and genomic data
STORAGE_loc = paste0(ROOT_loc, "/PLINK")
AERNA_loc = paste0(STORAGE_loc, "/_AE_ORIGINALS/AERNA")
AESCRNA_loc = paste0(STORAGE_loc, "/_AE_ORIGINALS/AESCRNA/prepped_data")
AEGSQC_loc = paste0(STORAGE_loc, "/_AE_ORIGINALS/AEGS_COMBINED_QC2018")

### SOME VARIABLES WE NEED DOWN THE LINE
TRAIT_OF_INTEREST = "HDAC9" # Phenotype
PROJECTNAME = "HDAC9"

cat("\nCreate a new analysis directory...\n")
ifelse(!dir.exists(file.path(PROJECT_loc, "/",PROJECTNAME)), 
       dir.create(file.path(PROJECT_loc, "/",PROJECTNAME)), 
       FALSE)
ANALYSIS_loc = paste0(PROJECT_loc,"/",PROJECTNAME)

ifelse(!dir.exists(file.path(ANALYSIS_loc, "/PLOTS")), 
       dir.create(file.path(ANALYSIS_loc, "/PLOTS")), 
       FALSE)
PLOT_loc = paste0(ANALYSIS_loc,"/PLOTS")

ifelse(!dir.exists(file.path(PLOT_loc, "/QC")), 
       dir.create(file.path(PLOT_loc, "/QC")), 
       FALSE)
QC_loc = paste0(PLOT_loc,"/QC")

ifelse(!dir.exists(file.path(ANALYSIS_loc, "/OUTPUT")), 
       dir.create(file.path(ANALYSIS_loc, "/OUTPUT")), 
       FALSE)
OUT_loc = paste0(ANALYSIS_loc, "/OUTPUT")

ifelse(!dir.exists(file.path(ANALYSIS_loc, "/BASELINE")), 
       dir.create(file.path(ANALYSIS_loc, "/BASELINE")), 
       FALSE)
BASELINE_loc = paste0(ANALYSIS_loc, "/BASELINE")


setwd(paste0(PROJECT_loc))
getwd()
list.files()

```

```{r Source functions}
source(paste0(PROJECT_loc, "/scripts/functions.R"))
```

```{r}
ggplot2::theme_set(ggplot2::theme_minimal())
pander::panderOptions("table.split.table", Inf)
```

```{r loading_packages, message=FALSE, warning=FALSE}
install.packages.auto("pander")
install.packages.auto("readr")
install.packages.auto("optparse")
install.packages.auto("tools")
install.packages.auto("dplyr")
install.packages.auto("tidyr")
install.packages.auto("naniar")

# To get 'data.table' with 'fwrite' to be able to directly write gzipped-files
# Ref: https://stackoverflow.com/questions/42788401/is-possible-to-use-fwrite-from-data-table-with-gzfile
# install.packages("data.table", repos = "https://Rdatatable.gitlab.io/data.table")
library(data.table)

install.packages.auto("tidyverse")
install.packages.auto("knitr")
install.packages.auto("DT")
install.packages.auto("eeptools")

install.packages.auto("openxlsx")

install.packages.auto("haven")
install.packages.auto("tableone")
install.packages.auto("sjPlot")

install.packages.auto("BlandAltmanLeh")

# Install the devtools package from Hadley Wickham
install.packages.auto('devtools')

# for plotting
install.packages.auto("pheatmap")
install.packages.auto("forestplot")
install.packages.auto("ggplot2")

install.packages.auto("ggpubr")

install.packages.auto("UpSetR")

devtools::install_github("thomasp85/patchwork")

# for Seurat etc
install.packages.auto("org.Hs.eg.db")
install.packages.auto("mygene")
install.packages.auto("EnhancedVolcano")
```

```{r}

# Install the devtools package from Hadley Wickham
install.packages.auto('devtools')
# Replace '2.3.4' with your desired version
# devtools::install_version(package = 'Seurat', version = package_version('2.3.4'))
# install.packages("Seurat")
install.packages.auto("Seurat") # latest version
library("Seurat")

```

```{r Setting: Colors}

Today = format(as.Date(as.POSIXlt(Sys.time())), "%Y%m%d")
Today.Report = format(as.Date(as.POSIXlt(Sys.time())), "%A, %B %d, %Y")

### UtrechtScienceParkColoursScheme
###
### WebsitetoconvertHEXtoRGB:http://hex.colorrrs.com.
### Forsomefunctionsyoushoulddividethesenumbersby255.
###
###	No.	Color			      HEX	(RGB)						              CHR		  MAF/INFO
###---------------------------------------------------------------------------------------
###	1	  yellow			    #FBB820 (251,184,32)				      =>	1		or 1.0>INFO
###	2	  gold			      #F59D10 (245,157,16)				      =>	2		
###	3	  salmon			    #E55738 (229,87,56)				      =>	3		or 0.05<MAF<0.2 or 0.4<INFO<0.6
###	4	  darkpink		    #DB003F ((219,0,63)				      =>	4		
###	5	  lightpink		    #E35493 (227,84,147)				      =>	5		or 0.8<INFO<1.0
###	6	  pink			      #D5267B (213,38,123)				      =>	6		
###	7	  hardpink		    #CC0071 (204,0,113)				      =>	7		
###	8	  lightpurple	    #A8448A (168,68,138)				      =>	8		
###	9	  purple			    #9A3480 (154,52,128)				      =>	9		
###	10	lavendel		    #8D5B9A (141,91,154)				      =>	10		
###	11	bluepurple		  #705296 (112,82,150)				      =>	11		
###	12	purpleblue		  #686AA9 (104,106,169)			      =>	12		
###	13	lightpurpleblue	#6173AD (97,115,173/101,120,180)	=>	13		
###	14	seablue			    #4C81BF (76,129,191)				      =>	14		
###	15	skyblue			    #2F8BC9 (47,139,201)				      =>	15		
###	16	azurblue		    #1290D9 (18,144,217)				      =>	16		or 0.01<MAF<0.05 or 0.2<INFO<0.4
###	17	lightazurblue	  #1396D8 (19,150,216)				      =>	17		
###	18	greenblue		    #15A6C1 (21,166,193)				      =>	18		
###	19	seaweedgreen	  #5EB17F (94,177,127)				      =>	19		
###	20	yellowgreen		  #86B833 (134,184,51)				      =>	20		
###	21	lightmossgreen	#C5D220 (197,210,32)				      =>	21		
###	22	mossgreen		    #9FC228 (159,194,40)				      =>	22		or MAF>0.20 or 0.6<INFO<0.8
###	23	lightgreen	  	#78B113 (120,177,19)				      =>	23/X
###	24	green			      #49A01D (73,160,29)				      =>	24/Y
###	25	grey			      #595A5C (89,90,92)				        =>	25/XY	or MAF<0.01 or 0.0<INFO<0.2
###	26	lightgrey		    #A2A3A4	(162,163,164)			      =>	26/MT
###
###	ADDITIONAL COLORS
###	27	midgrey			#D7D8D7
###	28	verylightgrey	#ECECEC"
###	29	white			#FFFFFF
###	30	black			#000000
###----------------------------------------------------------------------------------------------

uithof_color = c("#FBB820","#F59D10","#E55738","#DB003F","#E35493","#D5267B",
                 "#CC0071","#A8448A","#9A3480","#8D5B9A","#705296","#686AA9",
                 "#6173AD","#4C81BF","#2F8BC9","#1290D9","#1396D8","#15A6C1",
                 "#5EB17F","#86B833","#C5D220","#9FC228","#78B113","#49A01D",
                 "#595A5C","#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")

uithof_color_legend = c("#FBB820", "#F59D10", "#E55738", "#DB003F", "#E35493",
                        "#D5267B", "#CC0071", "#A8448A", "#9A3480", "#8D5B9A",
                        "#705296", "#686AA9", "#6173AD", "#4C81BF", "#2F8BC9",
                        "#1290D9", "#1396D8", "#15A6C1", "#5EB17F", "#86B833",
                        "#C5D220", "#9FC228", "#78B113", "#49A01D", "#595A5C",
                        "#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")
### ----------------------------------------------------------------------------
```

# ERA-CVD 'druggable-MI-targets'

<!-- ![ERA-CVD logo]("Users/swvanderlaan/iCloud/Genomics/Projects/#Druggable-MI-Genes/Administration/ERA-CVD\ Logo_CMYK.jpg") -->

For the ERA-CVD 'druggable-MI-targets' project (grantnumber: 01KL1802) we performed two related RNA sequencing (RNAseq) experiments:

1)  conventional ('bulk') RNAseq using RNA extracted from carotid plaque samples, n ± 700. As of `r Today.Report` all samples have been selected and
RNA has been extracted; quality control (QC) was performed and we have a dataset of 635 samples.

2)  single-cell RNAseq (scRNAseq) of at least n = 40 samples (20 females, 20 males). As of `r Today.Report` data is available of 40 samples (3 females, 15 males), we are extending sampling to get more female samples.

Plaque samples are derived from carotid endarterectomies as part of the [Athero-Express Biobank Study](http:www/atheroexpress.nl) which is an ongoing study in the UMC Utrecht.

# Background

Here we map the `r TRAIT_OF_INTEREST` to single-cells from the plaques.

```{r targets for mapping}

library(openxlsx)

gene_list_df <- read.xlsx(paste0(PROJECT_loc, "/targets/Genes.xlsx"), sheet = "Genes")

target_genes <- unlist(gene_list_df$Gene)
target_genes

```

# Load data

First we will load the data:

-   scRNAseq experimental data and rename the cell types.
-   Athero-Express clinical data.

Here we load the latest dataset from our Athero-Express single-cell RNA experiment.

```{r LoadData}

# load(paste0(AESCRNA_loc, "/20210811.46.patients.KP.RData"))
# scRNAseqData <- seuset
# rm(seuset)
# 
# saveRDS(scRNAseqData, paste0(AESCRNA_loc, "/20210811.46.patients.KP.RDS"))

scRNAseqData <- readRDS(paste0(AESCRNA_loc, "/20210811.46.patients.KP.RDS"))

scRNAseqData

```

The naming/classification is based on a combination conventional markers. We do not claim to know the exact identity of each cell, rather we refer to cells as 'KIT+ Mast cells"-like cells. Likewise we refer to the cell clusters as 'communities' of cells that exhibit similar properties, *i.e.* similar defining markers (*e.g. KIT*).

We will rename the cell types to human readable names.

```{r Change cell cummunity names}
### change names for clarity
backup.scRNAseqData = scRNAseqData
# get the old names to change to new names
UMAPPlot(scRNAseqData, label = FALSE, pt.size = 1.25, label.size = 4, group.by = "ident")

```

```{r}
unique(scRNAseqData@active.ident)
```

```{r}
celltypes <- c("CD68+CD4+ Monocytes" = "CD68+CD4+ Mono", 
               "CD68+IL18+TLR4+TREM2+ Resident macrophages" = "CD68+IL18+TLR4+TREM2+ MRes", 
               "CD68+CD1C+ Dendritic Cells" = "CD68+CD1C+ DC",
               "CD68+CASP1+IL1B+SELL+ Inflammatory macrophages" = "CD68+CASP1+IL1B+SELL MInf",
               "CD68+ABCA1+OLR1+TREM2+ Foam Cells" = "CD68+ABCA1+OLR1+TREM2+ FC",
               
               # T-cells
               "CD3+ T Cells I" = "CD3+ TC I",
               "CD3+ T Cells II" = "CD3+ TC II", 
               "CD3+ T Cells III" = "CD3+ TC III", 
               "CD3+ T Cells IV" = "CD3+ TC IV", 
               "CD3+ T Cells V" = "CD3+ TC V", 
               "CD3+ T Cells VI" = "CD3+ TC VI", 
               "FOXP3+ T Cells" = "FOXP3+ TC",
               
               # Endothelial cells
               "CD34+ Endothelial Cells I" = "CD34+ EC I", 
               "CD34+ Endothelial Cells II" = "CD34+ EC II", 
               
               # SMC
               "ACTA2+ Smooth Muscle Cells" = "ACTA2+ SMC", 
               
               # NK Cells
               "CD3+CD56+ NK Cells I" = "CD3+CD56+ NK I",
               "CD3+CD56+ NK Cells II" = "CD3+CD56+ NK II",
               # Mast
               "CD68+KIT+ Mast Cells" = "CD68+KIT+ MC",
               
               "CD79A+ Class-switched Memory B Cells" = "CD79A+ BCmem", 
               "CD79+ Plasma B Cells" = "CD79+ BCplasma")

scRNAseqData <- Seurat::RenameIdents(object = scRNAseqData, 
                                       celltypes)
```

```{r Change cell cummunity names - new plot}
UMAPPlot(scRNAseqData, label = TRUE, pt.size = 1.25, label.size = 4, group.by = "ident",
         repel = TRUE)

```

## Clinical data

Loading the Athero-Express clinical data.

```{r LoadAEDB}

AEDB.CEA <- readRDS(file = paste0(OUT_loc, "/20220317.HDAC9.AEDB.CEA.RDS"))

```


```{r }

# Baseline table variables
basetable_vars = c("Hospital", "ORyear", "Artery_summary",
                   "Age", "Gender", 
                   # "TC_finalCU", "LDL_finalCU", "HDL_finalCU", "TG_finalCU", 
                   "TC_final", "LDL_final", "HDL_final", "TG_final", 
                   # "hsCRP_plasma",
                   "systolic", "diastoli", "GFR_MDRD", "BMI", 
                   "KDOQI", "BMI_WHO",
                   "SmokerStatus", "AlcoholUse",
                   "DiabetesStatus", 
                   "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs", 
                   "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                   "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
                   "Symptoms.Update2G", "Symptoms.Update3G", "indexsymptoms_latest_4g",
                   "restenos", "stenose",
                   "CAD_history", "PAOD", "Peripheral.interv", 
                   "EP_composite", "EP_composite_time", "EP_major", "EP_major_time",
                   "MAC_rankNorm", "SMC_rankNorm", "Macrophages.bin", "SMC.bin",
                   "Neutrophils_rankNorm", "MastCells_rankNorm",
                   "IPH.bin", "VesselDensity_rankNorm",
                   "Calc.bin", "Collagen.bin", 
                   "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype", "Plaque_Vulnerability_Index")

basetable_bin = c("Gender",  "Artery_summary",
                  "KDOQI", "BMI_WHO",
                  "SmokerStatus", "AlcoholUse",
                  "DiabetesStatus", 
                  "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs", 
                  "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                  "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
                  "Symptoms.Update2G", "Symptoms.Update3G", "indexsymptoms_latest_4g",
                  "restenos", "stenose",
                  "CAD_history", "PAOD", "Peripheral.interv", 
                  "EP_major", "EP_composite", "Macrophages.bin", "SMC.bin",
                  "IPH.bin", 
                  "Calc.bin", "Collagen.bin", 
                  "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype", "Plaque_Vulnerability_Index")
# basetable_bin

basetable_con = basetable_vars[!basetable_vars %in% basetable_bin]
# basetable_con
```

## AESCRNA: baseline characteristics

### Preparation

```{r Baseline: creation}
metadata <- scRNAseqData@meta.data %>% as_tibble() %>% separate(orig.ident, c("Patient", NA))
scRNAseqDataMeta <- metadata %>% distinct(Patient, .keep_all = TRUE)

scRNAseqDataMetaAE <- merge(scRNAseqDataMeta, AEDB.CEA, by.x = "Patient", by.y = "STUDY_NUMBER", sort = FALSE, all.x = TRUE)
dim(scRNAseqDataMetaAE)

# Replace missing data 
# Ref: https://cran.r-project.org/web/packages/naniar/vignettes/replace-with-na.html
require(naniar)

na_strings <- c("NA", "N A", "N / A", "N/A", "N/ A", 
                "Not Available", "Not available", 
                "missing", 
                "-999", "-99", 
                "No data available/missing", "No data available/Missing")
# Then you write ~.x %in% na_strings - which reads as “does this value occur in the list of NA strings”.

scRNAseqDataMetaAE %>%
  replace_with_na_all(condition = ~.x %in% na_strings)
```

```{r }
cat("====================================================================================================")
cat("SELECTION THE SHIZZLE")

cat("- sanity checking PRIOR to selection")
library(data.table)
require(labelled)
ae.gender <- to_factor(scRNAseqDataMetaAE$Gender)
ae.hospital <- to_factor(scRNAseqDataMetaAE$Hospital)
table(ae.gender, ae.hospital, dnn = c("Sex", "Hospital"), useNA = "ifany")

ae.artery <- to_factor(scRNAseqDataMetaAE$Artery_summary)
table(ae.artery, ae.gender, dnn = c("Sex", "Artery"), useNA = "ifany")

ae.ic <- to_factor(scRNAseqDataMetaAE$informedconsent)
table(ae.ic, ae.gender, useNA = "ifany")

rm(ae.gender, ae.hospital, ae.artery, ae.ic)


scRNAseqDataMetaAE.all <- subset(scRNAseqDataMetaAE,
                                 (Artery_summary == "carotid (left & right)" | Artery_summary == "other carotid arteries (common, external)" ) & # we only want carotids
                                   informedconsent != "missing" & # we are really strict in selecting based on 'informed consent'!
                                   informedconsent != "no, died" &
                                   informedconsent != "yes, no tissue, no commerical business" &
                                   informedconsent != "yes, no tissue, no questionnaires, no medical info, no commercial business" &
                                   informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commerical business" &
                                   informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business" &
                                   informedconsent != "yes, no tissue, no health treatment" &
                                   informedconsent != "yes, no tissue, no questionnaires" &
                                   informedconsent != "yes, no tissue, health treatment when possible" &
                                   informedconsent != "yes, no tissue" &
                                   informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info" &
                                   informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commercial business" &
                                   informedconsent != "no, doesn't want to" &
                                   informedconsent != "no, unable to sign" &
                                   informedconsent != "no, no reaction" &
                                   informedconsent != "no, lost" &
                                   informedconsent != "no, too old" &
                                   informedconsent != "yes, no medical info, health treatment when possible" & 
                                   informedconsent != "no (never asked for IC because there was no tissue)" &
                                   informedconsent != "no, endpoint" &
                                   informedconsent != "nooit geincludeerd" & 
                                   informedconsent != "yes, no health treatment, no commercial business" & # IMPORTANT: since we are sharing with a commercial party
                                   informedconsent != "yes, no tissue, no commerical business" & 
                                   informedconsent != "yes, no tissue, no questionnaires, no medical info, no commercial business" & 
                                   informedconsent != "yes, no questionnaires, no health treatment, no commercial business" & 
                                   informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commerical business" & 
                                   informedconsent != "yes, no health treatment, no medical info, no commercial business" & 
                                   informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business" & 
                                   informedconsent != "yes, no commerical business" & 
                                   informedconsent != "yes, health treatment when possible, no commercial business" & 
                                   informedconsent != "yes, no medical info, no commercial business" & 
                                   informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commercial business" & 
                                   informedconsent != "yes, no questionnaires, no commercial business" & 
                                   informedconsent != "yes, no questionnaires, health treatment when possible, no commercial business" & 
                                   informedconsent != "second informed concents: yes, no commercial business")
# scRNAseqDataMetaAE.all[1:10, 1:10]
dim(scRNAseqDataMetaAE.all)
# DT::datatable(scRNAseqDataMetaAE.all)

```

### Baseline

Showing the baseline table for the scRNAseq data in 39 CEA patients with
informed consent.

```{r Baseline: Visualize}
cat("===========================================================================================")
cat("CREATE BASELINE TABLE")

# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
scRNAseqDataMetaAE.all.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                                  # factorVars = basetable_bin,
                                                  # strata = "Gender",
                                                  data = scRNAseqDataMetaAE.all, includeNA = TRUE), 
                                   nonnormal = c(), 
                                   quote = FALSE, showAllLevels = TRUE,
                                   format = "p", 
                                   contDigits = 3)[,1:2]

```

Writing the baseline table to Excel format.

```{r }
# Write basetable
require(openxlsx)
# write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AESCRNA.CEA.39pts.after_qc.IC_commercial.BaselineTable.xlsx"), 
#            format(scRNAseqDataMetaAE.all.tableOne, digits = 5, scientific = FALSE) , 
#            rowNames = TRUE, colNames = TRUE, overwrite = TRUE)

write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AESCRNA.CEA.39pts.after_qc.IC_academic.BaselineTable.xlsx"), 
           format(scRNAseqDataMetaAE.all.tableOne, digits = 5, scientific = FALSE) , 
           rowNames = TRUE, colNames = TRUE, overwrite = TRUE)

```

# AESCRNA

## Quality control

Here review the number of cells per sample, plate, and patients. And plot the
ratio's per sample and study number.

```{r QualityControl}
## check stuff
cat("\nHow many cells per type ...?")
sort(table(scRNAseqData@meta.data$SCT_snn_res.0.8))

# cat("\n\nHow many cells per plate ...?")
# sort(table(scRNAseqData@meta.data$ID))

# cat("\n\nHow many cells per type per plate ...?")
# table(scRNAseqData@meta.data$SCT_snn_res.0.8, scRNAseqData@meta.data$ID)

cat("\n\nHow many cells per patient ...?")
sort(table(scRNAseqData@meta.data$Patient))

cat("\n\nVisualizing these ratio's per study number and sample ...?")
UMAPPlot(scRNAseqData, label = TRUE, pt.size = 1.25, label.size = 4, group.by = "ident",
         repel = TRUE)
ggsave(paste0(PLOT_loc, "/", Today, ".UMAP.png"), plot = last_plot())
ggsave(paste0(PLOT_loc, "/", Today, ".UMAP.ps"), plot = last_plot())


# barplot(prop.table(x = table(scRNAseqData@active.ident, scRNAseqData@meta.data$Patient)), 
#         cex.axis = 1.0, cex.names = 0.5, las = 1,
#         col = uithof_color, xlab = "study number", legend.text = FALSE, args.legend = list(x = "bottom"))
# dev.copy(pdf, paste0(QC_loc, "/", Today, ".cell_ratios_per_sample.pdf"))
# dev.off()

# barplot(prop.table(x = table(scRNAseqData@active.ident, scRNAseqData@meta.data$ID)), 
#         cex.axis = 1.0, cex.names = 0.5, las = 2,
#         col = uithof_color, xlab = "sample ID", legend.text = FALSE, args.legend = list(x = "bottom"))
# dev.copy(pdf, paste0(QC_loc, "/", Today, ".cell_ratios_per_sample_per_plate.pdf"))
# dev.off()



```

## Visualisations

Let's project known cellular markers.

```{r Visualisation: tSNE Exploration}

UMAPPlot(scRNAseqData, label = FALSE, pt.size = 1.25, label.size = 4, group.by = "ident",
         repel = TRUE)

# endothelial cells
FeaturePlot(scRNAseqData, features = c("CD34"), cols =  c("#ECECEC", "#DB003F"))
FeaturePlot(scRNAseqData, features = c("EDN1"), cols =  c("#ECECEC", "#DB003F"))
FeaturePlot(scRNAseqData, features = c("EDNRA", "EDNRB"), cols =  c("#ECECEC", "#DB003F"))
FeaturePlot(scRNAseqData, features = c("CDH5", "PECAM1"), cols =  c("#ECECEC", "#DB003F"))
FeaturePlot(scRNAseqData, features = c("ACKR1"), cols =  c("#ECECEC", "#DB003F"))

# SMC
FeaturePlot(scRNAseqData, features = c("MYH11"), cols =  c("#ECECEC", "#DB003F"))
FeaturePlot(scRNAseqData, features = c("LGALS3", "ACTA2"), cols =  c("#ECECEC", "#DB003F"))

# macrophages
FeaturePlot(scRNAseqData, features = c("CD14", "CD68"), cols =  c("#ECECEC", "#DB003F"))
FeaturePlot(scRNAseqData, features = c("CD36"), cols =  c("#ECECEC", "#DB003F"))

# t-cells
FeaturePlot(scRNAseqData, features = c("CD3E"), cols =  c("#ECECEC", "#DB003F"))
FeaturePlot(scRNAseqData, features = c("CD4"), cols =  c("#ECECEC", "#DB003F"))
# FeaturePlot(scRNAseqData, features = c("CD8"), cols =  c("#ECECEC", "#DB003F"))

# b-cells
FeaturePlot(scRNAseqData, features = c("CD79A"), cols =  c("#ECECEC", "#DB003F"))

# mast cells
FeaturePlot(scRNAseqData, features = c("KIT"), cols =  c("#ECECEC", "#DB003F"))

# NK cells
FeaturePlot(scRNAseqData, features = c("NCAM1"), cols =  c("#ECECEC", "#DB003F"))

```

## Targets of interest:

We check whether the targets genes were sequenced using our method.

```{r list target genes}
length(target_genes)
target_genes

```

### Expression in cell communities

```{r Visualisation: preparation}

# target_genes_rm <- c("AC011294.3", "C6orf195", "C9orf53", "AL137026.1", "RP11-145E5.5",
#                      "ZNF32", "BCAM", "DUPD1", "PVRL2")
# 
# temp = target_genes[!target_genes %in% target_genes_rm]
# 
# target_genes_qc <- c(temp, "DUSP27", "NECTIN2")

target_genes_qc <- target_genes
target_genes_qc
```

```{r Visualisation: Targets Feature and Dot Plots, message=FALSE, warning=FALSE}
library(RColorBrewer)

p1 <- DotPlot(scRNAseqData, features = target_genes_qc,
        cols = "RdBu")

p1 + theme(axis.text.x = element_text(angle = 45, hjust=1, size = 5))

ggsave(paste0(PLOT_loc, "/", Today, ".DotPlot.Targets.png"), plot = last_plot())
ggsave(paste0(PLOT_loc, "/", Today, ".DotPlot.Targets.ps"), plot = last_plot())
ggsave(paste0(PLOT_loc, "/", Today, ".DotPlot.Targets.pdf"), plot = last_plot())

rm(p1)

# FeaturePlot(scRNAseqData, features = c(target_genes_qc),
#             cols =  c("#ECECEC", "#DB003F", "#9A3480","#1290D9"),
#             combine = TRUE)
# 
# ggsave(paste0(PLOT_loc, "/", Today, ".FeaturePlot.Targets.png"), plot = last_plot())
# ggsave(paste0(PLOT_loc, "/", Today, ".FeaturePlot.Targets.ps"), plot = last_plot())


```

```{r Visualisation: Targets}
# VlnPlot(scRNAseqData, features = "DUSP27")

# VlnPlot files
ifelse(!dir.exists(file.path(PLOT_loc, "/VlnPlot")), 
       dir.create(file.path(PLOT_loc, "/VlnPlot")), 
       FALSE)
VlnPlot_loc = paste0(PLOT_loc, "/VlnPlot")


for (GENE in target_genes_qc){
  print(paste0("Projecting the expression of ", GENE, "."))

  vp1 <-  VlnPlot(scRNAseqData, features = GENE) + 
    xlab("cell communities") + 
    ylab(bquote("normalized expression")) +
    theme(axis.title.x = element_text(color = "#000000", size = 14, face = "bold"), 
            axis.title.y = element_text(color = "#000000", size = 14, face = "bold"), 
            legend.position = "none")
    ggsave(paste0(VlnPlot_loc, "/", Today, ".VlnPlot.",GENE,".png"), plot = last_plot())
    ggsave(paste0(VlnPlot_loc, "/", Today, ".VlnPlot.",GENE,".ps"), plot = last_plot())
    ggsave(paste0(VlnPlot_loc, "/", Today, ".VlnPlot.",GENE,".pdf"), plot = last_plot())
  
  # print(vp1)
  
}

```

### Differential expression between cell communities

Here we project genes to only the broad cell communities:

-   macrophages
-   endothelial cells
-   smooth muscle cells
-   T-cells
-   B-cells
-   Mast cells
-   NK-cells
-   Mixed cells

#### Macrophages

```{r}
unique(scRNAseqData@active.ident)
```

Comparison between the macrophages cell communities (*CD14/CD68*<sup>+</sup>),
and all other communities.

```{r Visualisation: Volcano MAC calculate}

MAC.markers <- FindMarkers(object = scRNAseqData, 
                          ident.1 = c("CD68+CASP1+IL1B+SELL MInf", 
                                      "CD68+CD1C+ DC", 
                                      "CD68+CD4+ Mono",
                                      "CD68+IL18+TLR4+TREM2+ MRes",
                                      "CD68+ABCA1+OLR1+TREM2+ FC"), 
                          ident.2 = c(#"CD68+CASP1+IL1B+SELL MInf", 
                                      #"CD68+CD1C+ DC", 
                                      #"CD68+CD4+ Mono",
                                      #"CD68+IL18+TLR4+TREM2+ MRes",
                                      #"CD68+ABCA1+OLR1+TREM2+ FC",
                                      "CD3+ TC I",
                                      "CD3+ TC II", 
                                      "CD3+ TC III", 
                                      "CD3+ TC IV", 
                                      "CD3+ TC V", 
                                      "CD3+ TC VI", 
                                      "FOXP3+ TC", 
                                      "CD34+ EC I", 
                                      "CD34+ EC II",
                                      "ACTA2+ SMC", 
                                      "CD3+CD56+ NK I",
                                      "CD3+CD56+ NK II", 
                                      "CD68+KIT+ MC",
                                      "CD79+ BCplasma", 
                                      "CD79A+ BCmem"))

DT::datatable(MAC.markers)
```

```{r Visualisation: Volcano MAC, message=FALSE, warning=FALSE}
MAC_Volcano_TargetsA = EnhancedVolcano(MAC.markers,
    lab = rownames(MAC.markers),
    x = "avg_log2FC",
    y = "p_val_adj",
    selectLab = target_genes_qc,
    axisLabSize = 12,
    xlab = "average fold-change",
    title = "Macrophage markers\n(Macrophage communities vs the rest)",
    titleLabSize = 14,
    pCutoff = 0.05/(nrow(MAC.markers)), # 20552 genes
    FCcutoff = 1.25,
    pointSize = 1.5,
    labSize = 3.0,
    legendLabels =c('NS','avg. fold-change','P',
      'P & avg. fold-change'),
    legendPosition = "right",
    legendLabSize = 10,
    legendIconSize = 3.0,
    drawConnectors = TRUE,
    widthConnectors = 0.2,
    colConnectors = "#595A5C",
    gridlines.major = FALSE,
    gridlines.minor = FALSE)
MAC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.MAC.DEG.Targets.pdf"), 
       plot = MAC_Volcano_TargetsA)
```

The target results are given below and written to a file.

```{r Results MAC}
library(tibble)
MAC.markers <- add_column(MAC.markers, Gene = row.names(MAC.markers), .before = 1)

temp <- MAC.markers[MAC.markers$Gene %in% target_genes_qc,]

DT::datatable(temp)
```

```{r Results MAC: writing}
fwrite(temp, file = paste0(OUT_loc, "/", Today, ".MAC.DEG.Targets.txt"),
       quote = FALSE,
       sep = "\t", 
       showProgress = FALSE, verbose = FALSE)
```

#### Smooth muscle cells

Comparison between the smooth muscle cell communities (*ACTA2*<sup>+</sup>), and
all other communities.

```{r Visualisation: Volcano SMC calculate}

SMC.markers <- FindMarkers(object = scRNAseqData, 
                          ident.1 = c("ACTA2+ SMC"), 
                          ident.2 = c("CD68+CASP1+IL1B+SELL MInf", 
                                      "CD68+CD1C+ DC", 
                                      "CD68+CD4+ Mono",
                                      "CD68+IL18+TLR4+TREM2+ MRes",
                                      "CD68+ABCA1+OLR1+TREM2+ FC",
                                      "CD3+ TC I",
                                      "CD3+ TC II", 
                                      "CD3+ TC III", 
                                      "CD3+ TC IV", 
                                      "CD3+ TC V", 
                                      "CD3+ TC VI", 
                                      "FOXP3+ TC", 
                                      "CD34+ EC I", 
                                      "CD34+ EC II",
                                      #"ACTA2+ SMC", 
                                      "CD3+CD56+ NK I",
                                      "CD3+CD56+ NK II", 
                                      "CD68+KIT+ MC",
                                      "CD79+ BCplasma", 
                                      "CD79A+ BCmem"))

DT::datatable(SMC.markers)
```

```{r Visualisation: Volcano SMC, message=FALSE, warning=FALSE}
SMC_Volcano_TargetsA = EnhancedVolcano(SMC.markers,
    lab = rownames(SMC.markers),
    x = "avg_log2FC",
    y = "p_val_adj",
    selectLab = target_genes_qc,
    axisLabSize = 12,
    xlab = "average fold-change",
    title = "SMC markers\n(SMC communities vs the rest)",
    titleLabSize = 14,
    pCutoff = 0.05/(nrow(SMC.markers)), # 20552 genes
    FCcutoff = 1.25,
    pointSize = 1.5,
    labSize = 3.0,
    legendLabels =c('NS','avg. fold-change','P',
      'P & avg. fold-change'),
    legendPosition = "right",
    legendLabSize = 10,
    legendIconSize = 3.0,
    drawConnectors = TRUE,
    widthConnectors = 0.2,
    colConnectors = "#595A5C",
    gridlines.major = FALSE,
    gridlines.minor = FALSE)
SMC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.SMC.DEG.Targets.pdf"), 
       plot = SMC_Volcano_TargetsA)
```

The target results are given below and written to a file.

```{r Results SMC}
library(tibble)
SMC.markers <- add_column(SMC.markers, Gene = row.names(SMC.markers), .before = 1)

temp <- SMC.markers[SMC.markers$Gene %in% target_genes_qc,]

DT::datatable(temp)
```

```{r Results SMC: writing}
fwrite(temp, file = paste0(OUT_loc, "/", Today, ".SMC.DEG.Targets.txt"),
       quote = FALSE,
       sep = "\t", 
       showProgress = FALSE, verbose = FALSE)
```

#### Endothelial cells

Comparison between the endothelial cell communities (*CD34*<sup>+</sup>), and
all other communities.

```{r Visualisation: Volcano EC calculate}

EC.markers <- FindMarkers(object = scRNAseqData, 
                          ident.1 = c("CD34+ EC I", 
                                      "CD34+ EC II"), 
                          ident.2 = c("CD68+CASP1+IL1B+SELL MInf", 
                                      "CD68+CD1C+ DC", 
                                      "CD68+CD4+ Mono",
                                      "CD68+IL18+TLR4+TREM2+ MRes",
                                      "CD68+ABCA1+OLR1+TREM2+ FC",
                                      "CD3+ TC I",
                                      "CD3+ TC II", 
                                      "CD3+ TC III", 
                                      "CD3+ TC IV", 
                                      "CD3+ TC V", 
                                      "CD3+ TC VI", 
                                      "FOXP3+ TC", 
                                      # "CD34+ EC I", 
                                      # "CD34+ EC II",
                                      "ACTA2+ SMC", 
                                      "CD3+CD56+ NK I",
                                      "CD3+CD56+ NK II", 
                                      "CD68+KIT+ MC",
                                      "CD79+ BCplasma", 
                                      "CD79A+ BCmem"))

DT::datatable(EC.markers)
```

```{r Visualisation: Volcano EC, message=FALSE, warning=FALSE}
EC_Volcano_TargetsA = EnhancedVolcano(EC.markers,
    lab = rownames(EC.markers),
    x = "avg_log2FC",
    y = "p_val_adj",
    selectLab = target_genes_qc,
    axisLabSize = 12,
    xlab = "average fold-change",
    title = "Endothelial cell markers\n(EC communities vs the rest)",
    titleLabSize = 14,
    pCutoff = 0.05/(nrow(EC.markers)), # 20552 genes
    FCcutoff = 1.25,
    pointSize = 1.5,
    labSize = 3.0,
    legendLabels =c('NS','avg. fold-change','P',
      'P & avg. fold-change'),
    legendPosition = "right",
    legendLabSize = 10,
    legendIconSize = 3.0,
    drawConnectors = TRUE,
    widthConnectors = 0.2,
    colConnectors = "#595A5C",
    gridlines.major = FALSE,
    gridlines.minor = FALSE)
EC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.EC.DEG.Targets.pdf"), 
       plot = EC_Volcano_TargetsA)
```

The target results are given below and written to a file.

```{r Results EC}
library(tibble)
EC.markers <- add_column(EC.markers, Gene = row.names(EC.markers), .before = 1)

temp <- EC.markers[EC.markers$Gene %in% target_genes_qc,]

DT::datatable(temp)
```

```{r Results EC: writing}
fwrite(temp, file = paste0(OUT_loc, "/", Today, ".EC.DEG.Targets.txt"),
       quote = FALSE,
       sep = "\t", 
       showProgress = FALSE, verbose = FALSE)
```

#### T-cells

Comparison between the T-cell communities (*CD3/CD4/CD8*<sup>+</sup>), and all
other communities.

```{r Visualisation: Volcano Tcell calculate}

TC.markers <- FindMarkers(object = scRNAseqData, 
                          ident.1 = c("CD3+ TC I",
                                      "CD3+ TC II", 
                                      "CD3+ TC III", 
                                      "CD3+ TC IV", 
                                      "CD3+ TC V", 
                                      "CD3+ TC VI", 
                                      "FOXP3+ TC"), 
                          ident.2 = c("CD68+CASP1+IL1B+SELL MInf", 
                                      "CD68+CD1C+ DC", 
                                      "CD68+CD4+ Mono",
                                      "CD68+IL18+TLR4+TREM2+ MRes",
                                      "CD68+ABCA1+OLR1+TREM2+ FC",
                                      # "CD3+ TC I",
                                      # "CD3+ TC II", 
                                      # "CD3+ TC III", 
                                      # "CD3+ TC IV", 
                                      # "CD3+ TC V", 
                                      # "CD3+ TC VI", 
                                      # "FOXP3+ TC", 
                                      "CD34+ EC I", 
                                      "CD34+ EC II",
                                      "ACTA2+ SMC", 
                                      "CD3+CD56+ NK I",
                                      "CD3+CD56+ NK II", 
                                      "CD68+KIT+ MC",
                                      "CD79+ BCplasma", 
                                      "CD79A+ BCmem"))

DT::datatable(TC.markers)
```

```{r Visualisation: Volcano Tcell, message=FALSE, warning=FALSE}
TC_Volcano_TargetsA = EnhancedVolcano(TC.markers,
    lab = rownames(TC.markers),
    x = "avg_log2FC",
    y = "p_val_adj",
    selectLab = target_genes_qc,
    axisLabSize = 12,
    xlab = "average fold-change",
    title = "T-cell markers\n(T-cell communities vs the rest)",
    titleLabSize = 14,
    pCutoff = 0.05/nrow(TC.markers), # 20552 genes
    FCcutoff = 1.25,
    pointSize = 1.5,
    labSize = 3.0,
    legendLabels =c('NS','avg. fold-change','P',
      'P & avg. fold-change'),
    legendPosition = "right",
    legendLabSize = 10,
    legendIconSize = 3.0,
    drawConnectors = TRUE,
    widthConnectors = 0.2,
    colConnectors = "#595A5C",
    gridlines.major = FALSE,
    gridlines.minor = FALSE)
TC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.TC.DEG.Targets.pdf"), 
       plot = TC_Volcano_TargetsA)
```

The target results are given below and written to a file.

```{r Results TC}
library(tibble)
TC.markers <- add_column(TC.markers, Gene = row.names(TC.markers), .before = 1)

temp <- TC.markers[TC.markers$Gene %in% target_genes_qc,]

DT::datatable(temp)
```

```{r Results TC: writing}
fwrite(temp, file = paste0(OUT_loc, "/", Today, ".TC.DEG.Targets.txt"),
       quote = FALSE,
       sep = "\t", 
       showProgress = FALSE, verbose = FALSE)
```

#### B-cells

Comparison between the B-cell communities (*CD79A*<sup>+</sup>), and all other
communities.

```{r Visualisation: Volcano Bcell calculate}

BC.markers <- FindMarkers(object = scRNAseqData, 
                          ident.1 = c("CD79+ BCplasma", 
                                      "CD79A+ BCmem"), 
                          ident.2 = c("CD68+CASP1+IL1B+SELL MInf", 
                                      "CD68+CD1C+ DC", 
                                      "CD68+CD4+ Mono",
                                      "CD68+IL18+TLR4+TREM2+ MRes",
                                      "CD68+ABCA1+OLR1+TREM2+ FC",
                                      "CD3+ TC I",
                                      "CD3+ TC II", 
                                      "CD3+ TC III", 
                                      "CD3+ TC IV", 
                                      "CD3+ TC V", 
                                      "CD3+ TC VI", 
                                      "FOXP3+ TC", 
                                      "CD34+ EC I", 
                                      "CD34+ EC II",
                                      "ACTA2+ SMC", 
                                      "CD3+CD56+ NK I",
                                      "CD3+CD56+ NK II", 
                                      "CD68+KIT+ MC"
                                      # "CD79+ BCplasma", 
                                      # "CD79A+ BCmem"
                                      ))

DT::datatable(BC.markers)
```

```{r Visualisation: Volcano Bcell, message=FALSE, warning=FALSE}
BC_Volcano_TargetsA = EnhancedVolcano(BC.markers,
    lab = rownames(BC.markers),
    x = "avg_log2FC",
    y = "p_val_adj",
    selectLab = target_genes_qc,
    axisLabSize = 12,
    xlab = "average fold-change",
    title = "B-cell markers\n(B-cell communities vs the rest)",
    titleLabSize = 14,
    pCutoff = 0.05/nrow(BC.markers), # 20552 genes
    FCcutoff = 1.25,
    pointSize = 1.5,
    labSize = 3.0,
    legendLabels =c('NS','avg. fold-change','P',
      'P & avg. fold-change'),
    legendPosition = "right",
    legendLabSize = 10,
    legendIconSize = 3.0,
    drawConnectors = TRUE,
    widthConnectors = 0.2,
    colConnectors = "#595A5C",
    gridlines.major = FALSE,
    gridlines.minor = FALSE)
BC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.BC.DEG.Targets.pdf"), 
       plot = BC_Volcano_TargetsA)
```

The target results are given below and written to a file.

```{r Results BC}
library(tibble)
BC.markers <- add_column(BC.markers, Gene = row.names(BC.markers), .before = 1)

temp <- BC.markers[BC.markers$Gene %in% target_genes_qc,]

DT::datatable(temp)
```

```{r Results BC: writing}
fwrite(temp, file = paste0(OUT_loc, "/", Today, ".BC.DEG.Targets.txt"),
       quote = FALSE,
       sep = "\t", 
       showProgress = FALSE, verbose = FALSE)
```

#### Mast cells

Comparison between the mast cell communities (*KIT*<sup>+</sup>), and all other
communities.

```{r Visualisation: Volcano Mast calculate}

MC.markers <- FindMarkers(object = scRNAseqData, 
                          ident.1 = c("CD68+KIT+ MC"), 
                          ident.2 = c("CD68+CASP1+IL1B+SELL MInf", 
                                      "CD68+CD1C+ DC", 
                                      "CD68+CD4+ Mono",
                                      "CD68+IL18+TLR4+TREM2+ MRes",
                                      "CD68+ABCA1+OLR1+TREM2+ FC",
                                      "CD3+ TC I",
                                      "CD3+ TC II", 
                                      "CD3+ TC III", 
                                      "CD3+ TC IV", 
                                      "CD3+ TC V", 
                                      "CD3+ TC VI", 
                                      "FOXP3+ TC", 
                                      "CD34+ EC I", 
                                      "CD34+ EC II",
                                      "ACTA2+ SMC", 
                                      "CD3+CD56+ NK I",
                                      "CD3+CD56+ NK II", 
                                      # "CD68+KIT+ MC",
                                      "CD79+ BCplasma", 
                                      "CD79A+ BCmem"))

DT::datatable(MC.markers)
```

```{r Visualisation: Volcano Mast, message=FALSE, warning=FALSE}
MC_Volcano_TargetsA = EnhancedVolcano(MC.markers,
    lab = rownames(MC.markers),
    x = "avg_log2FC",
    y = "p_val_adj",
    selectLab = target_genes_qc,
    axisLabSize = 12,
    xlab = "average fold-change",
    title = "Mast cell markers\n(Mast cell communities vs the rest)",
    titleLabSize = 14,
    pCutoff = 0.05/nrow(MC.markers), # 20552 genes
    FCcutoff = 1.25,
    pointSize = 1.5,
    labSize = 3.0,
    legendLabels =c('NS','avg. fold-change','P',
      'P & avg. fold-change'),
    legendPosition = "right",
    legendLabSize = 10,
    legendIconSize = 3.0,
    drawConnectors = TRUE,
    widthConnectors = 0.2,
    colConnectors = "#595A5C",
    gridlines.major = FALSE,
    gridlines.minor = FALSE)
MC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.MC.DEG.Targets.pdf"), 
       plot = MC_Volcano_TargetsA)
```

The target results are given below and written to a file.

```{r Results MC}
library(tibble)
MC.markers <- add_column(MC.markers, Gene = row.names(MC.markers), .before = 1)

temp <- MC.markers[MC.markers$Gene %in% target_genes_qc,]

DT::datatable(temp)
```

```{r Results MC: writing}
fwrite(temp, file = paste0(OUT_loc, "/", Today, ".MC.DEG.Targets.txt"),
       quote = FALSE,
       sep = "\t", 
       showProgress = FALSE, verbose = FALSE)
```

#### NK-cells

Comparison between the natural killer cell communities (*NCAM1*<sup>+</sup>),
and all other communities.

```{r Visualisation: Volcano NK calculate}

NK.markers <- FindMarkers(object = scRNAseqData, 
                          ident.1 = c("CD3+CD56+ NK I",
                                      "CD3+CD56+ NK II"), 
                          ident.2 = c("CD68+CASP1+IL1B+SELL MInf", 
                                      "CD68+CD1C+ DC", 
                                      "CD68+CD4+ Mono",
                                      "CD68+IL18+TLR4+TREM2+ MRes",
                                      "CD68+ABCA1+OLR1+TREM2+ FC",
                                      "CD3+ TC I",
                                      "CD3+ TC II", 
                                      "CD3+ TC III", 
                                      "CD3+ TC IV", 
                                      "CD3+ TC V", 
                                      "CD3+ TC VI", 
                                      "FOXP3+ TC", 
                                      "CD34+ EC I", 
                                      "CD34+ EC II",
                                      "ACTA2+ SMC", 
                                      # "CD3+CD56+ NK I",
                                      # "CD3+CD56+ NK II", 
                                      "CD68+KIT+ MC",
                                      "CD79+ BCplasma", 
                                      "CD79A+ BCmem"))

DT::datatable(NK.markers)
```

```{r Visualisation: Volcano NK, message=FALSE, warning=FALSE}
NK_Volcano_TargetsA = EnhancedVolcano(NK.markers,
    lab = rownames(NK.markers),
    x = "avg_log2FC",
    y = "p_val_adj",
    selectLab = target_genes_qc,
    axisLabSize = 12,
    xlab = "average fold-change",
    title = "NK markers\n(NK-cell communities vs the rest)",
    titleLabSize = 14,
    pCutoff = 0.05/nrow(NK.markers), # 20552 genes
    FCcutoff = 1.25,
    pointSize = 1.5,
    labSize = 3.0,
    legendLabels =c('NS','avg. fold-change','P',
      'P & avg. fold-change'),
    legendPosition = "right",
    legendLabSize = 10,
    legendIconSize = 3.0,
    drawConnectors = TRUE,
    widthConnectors = 0.2,
    colConnectors = "#595A5C",
    gridlines.major = FALSE,
    gridlines.minor = FALSE)
NK_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.NK.DEG.Targets.pdf"), 
       plot = NK_Volcano_TargetsA)
```

The target results are given below and written to a file.

```{r Results NK}
library(tibble)
NK.markers <- add_column(NK.markers, Gene = row.names(NK.markers), .before = 1)

temp <- NK.markers[NK.markers$Gene %in% target_genes_qc,]

DT::datatable(temp)
```

```{r Results NK: writing}
fwrite(temp, file = paste0(OUT_loc, "/", Today, ".NK.DEG.Targets.txt"),
       quote = FALSE,
       sep = "\t", 
       showProgress = FALSE, verbose = FALSE)
```

# Subset scRNAseq data

List of samples to be included based on informed consent (see above).

```{r}
samples_of_interest <- unlist(scRNAseqDataMetaAE.all$Patient)

```

```{r}
scRNAseqDataCEA39 <- subset(scRNAseqData, subset = Patient %in% samples_of_interest)
```

```{r}
variables_of_interest <- c("Hospital", "ORyear", "Artery_summary",
                           "Age", "Gender",
                           "TC_final", "LDL_final", "HDL_final", "TG_final",
                           "systolic", "diastoli", "GFR_MDRD", "BMI",
                           "KDOQI", "BMI_WHO",
                           "SmokerStatus", "AlcoholUse",
                           "DiabetesStatus",
                           "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs",
                           "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD",
                           "Stroke_Dx",
                           "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
                           "Symptoms.Update2G", "Symptoms.Update3G", "indexsymptoms_latest_4g",
                           "restenos", "stenose",
                           "CAD_history", "PAOD", "Peripheral.interv",
                           "EP_composite", "EP_composite_time", "EP_major", "EP_major_time")

temp <- subset(scRNAseqDataMetaAE.all, select = c("Patient", variables_of_interest))
# str(temp)

```

```{r}
scRNAseqDataCEA39@meta.data <- merge(scRNAseqDataCEA39@meta.data, temp, by.x = "Patient", by.y = "Patient")
scRNAseqDataCEA39@meta.data <- dplyr::rename(scRNAseqDataCEA39@meta.data, "STUDY_NUMBER" = "Patient")

# str(scRNAseqDataCEA39@meta.data)

```

## Saving new dataset

```{r}
temp2 <- as_tibble(subset(scRNAseqDataCEA39@meta.data, select = c("STUDY_NUMBER", "orig.ident", "nCount_RNA", "nFeature_RNA",
                                                                 "Plate", "Batch", "C.H", "Type", "percent.mt",
                                                                 "nCount_SCT", "nFeature_SCT", "seurat_clusters")))

# fwrite(temp2,
#        file = paste0(OUT_loc, "/", Today, ".AESCRNA.CEA.39pts.samplelist.after_qc.IC_commercial.csv"),
#        sep = ",", row.names = FALSE, col.names = TRUE,
#        showProgress = TRUE)
# rm(temp2)
# 
# temp <- dplyr::rename(temp, "STUDY_NUMBER" = "Patient")
# fwrite(temp,
#        file = paste0(OUT_loc, "/", Today, ".AESCRNA.CEA.39pts.clinicaldata.after_qc.IC_commercial.csv"),
#        sep = ",", row.names = FALSE, col.names = TRUE,
#        showProgress = TRUE)
# rm(temp)
# 
# saveRDS(scRNAseqDataCEA39, file = paste0(OUT_loc, "/", Today, ".AESCRNA.CEA.39pts.Seurat.after_qc.IC_commercial.RDS"))

fwrite(temp2,
       file = paste0(OUT_loc, "/", Today, ".AESCRNA.CEA.39pts.samplelist.after_qc.IC_academic.csv"),
       sep = ",", row.names = FALSE, col.names = TRUE,
       showProgress = TRUE)
rm(temp2)

temp <- dplyr::rename(temp, "STUDY_NUMBER" = "Patient")
fwrite(temp,
       file = paste0(OUT_loc, "/", Today, ".AESCRNA.CEA.39pts.clinicaldata.after_qc.IC_academic.csv"),
       sep = ",", row.names = FALSE, col.names = TRUE,
       showProgress = TRUE)
rm(temp)

saveRDS(scRNAseqDataCEA39, file = paste0(OUT_loc, "/", Today, ".AESCRNA.CEA.39pts.Seurat.after_qc.IC_academic.RDS"))

```


# Session information

--------------------------------------------------------------------------------

    Version:      v1.0.0
    Last update:  2022-03-17
    Written by:   Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
    Description:  Script to load single-cell RNA sequencing (scRNAseq) data, and perform quality control (QC), and initial mapping to cells.
    Minimum requirements: R version 3.5.2 (2018-12-20) -- 'Eggshell Igloo', macOS Mojave (10.14.2).

    Change log
    * v1.0.0 Initial version.

--------------------------------------------------------------------------------

```{r eval = TRUE}
sessionInfo()
```

# Saving environment

```{r Saving}
rm(backup.scRNAseqData)
rm(scRNAseqData, scRNAseqDataCEA39)

save.image(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".AESCRNA.results.RData"))

```

+---------------------------------------------------------------------------------------------------------------------------------------+
| <sup>© 1979-2022 Sander W. van der Laan | s.w.vanderlaan[at]gmail.com [swvanderlaan.github.io](https://swvanderlaan.github.io).</sup> |
+---------------------------------------------------------------------------------------------------------------------------------------+
